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LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

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In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTB-TIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 730K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. To evaluate a tracker on different attributes, we define 4 scenario attributes and 12 challenge attributes in the evaluation dataset. By releasing LSOTB-TIR, we encourage the community to develop deep learning based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance. Furthermore, we retrain several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.
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LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared
Object Tracking Benchmark
Qiao Liu
Xin Li
Harbin Institute of Technology,
Shenzhen
liuqiao.hit@gmail.com
Zhenyu He
Harbin Institute of Technology,
Shenzhen
Peng Cheng Laboratory
zhenyuhe@hit.edu.cn
Chenglong Li
Anhui University
chenglongli@ahu.edu.cn
Jun Li
Zikun Zhou
Di Yuan
Harbin Institute of Technology,
Shenzhen
Jing Li
Kai Yang
Nana Fan
Harbin Institute of Technology,
Shenzhen
Feng Zheng
Southern University of Science and
Technology
zfeng02@gmail.com
ABSTRACT
In this paper, we present a Large-Scale and high-diversity general
Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTB-
TIR, which consists of an evaluation dataset and a training dataset
with a total of 1,400 TIR sequences and more than 600K frames.
We annotate the bounding box of objects in every frame of all se-
quences and generate over 730K bounding boxes in total. To the
best of our knowledge, LSOTB-TIR is the largest and most diverse
TIR object tracking benchmark to date. To evaluate a tracker on
dierent attributes, we dene 4 scenario attributes and 12 chal-
lenge attributes in the evaluation dataset. By releasing LSOTB-TIR,
we encourage the community to develop deep learning based TIR
trackers and evaluate them fairly and comprehensively. We evalu-
ate and analyze more than 30 trackers on LSOTB-TIR to provide a
series of baselines, and the results show that deep trackers achieve
promising performance. Furthermore, we re-train several represen-
tative deep trackers on LSOTB-TIR, and their results demonstrate
that the proposed training dataset signicantly improves the per-
formance of deep TIR trackers. Codes and dataset are available at
https://github.com/QiaoLiuHit/LSOTB-TIR.
CCS CONCEPTS
Computing methodologies Tracking
;
Image representa-
tions.
KEYWORDS
thermal infrared dataset, thermal infrared object tracking, deep
representation learning
Corresponding author.
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MM ’20, October 12–16, 2020, Seattle, WA, USA
©2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-7988-5/20/10. . . $15.00
https://doi.org/10.1145/3394171.3413922
ACM Reference Format:
Qiao Liu, Xin Li, Zhenyu He, Chenglong Li, Jun Li, Zikun Zhou, Di Yuan,
Jing Li, Kai Yang, Nana Fan, and Feng Zheng. 2020. LSOTB-TIR: A Large-
Scale High-Diversity Thermal Infrared Object Tracking Benchmark. In
Proceedings of the 28th ACM International Conference on Multimedia (MM ’20),
October 12–16, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 10 pages.
https://doi.org/10.1145/3394171.3413922
1 INTRODUCTION
TIR object tracking is an important task in articial intelligence.
Given an initial position of a TIR object in the rst frame, TIR
object tracking is to locate the object in the rest of the frames of
a sequence. With the popularization of civilian thermal imaging
devices, TIR object tracking receives more and more attention as
a crucial intelligent vision technology. It is widely used in video
surveillance, maritime rescue, and driver assistance at night [
18
]
since it can track the object in total darkness. In the past several
years, some TIR object tracking methods [
20
,
23
,
34
,
39
,
60
,
61
]
are proposed. Despite much progress, TIR object tracking faces
many unsolved problems, such as distractor, intensity variation,
and thermal crossover [37].
Evaluating a tracker fairly and comprehensively on a benchmark
is crucial to the development of TIR object tracking. However, cur-
rently widely used TIR object tracking benchmarks, e.g., LTIR [
3
],
VOT-TIR16 [
17
], and PTB-TIR [
37
] suer from the following draw-
backs that make them less eective in conducting a fair and com-
prehensive evaluation. First, their scale is too small to make an
eective evaluation because a tracker can easily overt to a small
dataset using the parameter ne-tuning. Second, they have too few
kinds of objects, e.g., PTB-TIR only contains pedestrian objects,
which cannot provide an evaluation on general TIR objects. Third,
they only have a few tracking scenarios and challenges, which does
not meet the requirements of real-world applications. Therefore, it
is imperative to build a larger and more diverse TIR object tracking
benchmark.
Recently, motivated by the success of deep learning in most vi-
sual tasks, several attempts [
20
,
34
,
38
,
39
] incorporate deep feature
models for TIR object tracking and achieve some success. However,
the used deep feature models are learned from RGB images, and
we nd by experiments that these RGB based deep feature models
LTIR VOT-TIR16 PTB-TIR RGB-T LSOTB-TIR
Sequence Frame Class Challenge Scenario
Number
20
25
60
234
1400
11K
14K
30K
117K
606K
1
6
8
47
6
9
12
4
Figure 1: Comparison of currently widely used TIR object
tracking benchmarks, including LTIR [3], VOT-TIR16 [17],
PTB-TIR [37], RGB-T [31], and LSOTB-TIR. Among these
benchmarks, LSOTB-TIR is the largest and most diverse.
Noting that only LSOTB-TIR contains the scenario attribute.
are less eective in representing TIR objects, as shown in Fig. 2.
Dierent from RGB images, TIR images do not have color informa-
tion and lack texture features, hence it is crucial to use ne-grained
features, such as local contour and structure, to distinguish objects
in TIR images. Furthermore, we nd by experiments that learning
TIR-specic deep features for representing further promotes the
performance of TIR object tracking. However, the lacking of a large-
scale TIR dataset for deep model training hinders the advantage of
the deep leaning in TIR object tracking.
To address the above-mentioned issues, we develop a large-scale
and high-diversity TIR object tracking benchmark, called LSOTB-
TIR, which consists of an evaluation dataset and a training dataset
with a total of 1,400 TIR sequences and more than 600K frames.
We annotate the bounding box of objects in every frame of all se-
quences and generate more than 730K bounding boxes in total. We
carefully select 120 sequences with 22 object classes and more than
82K frames as the evaluation dataset, which is larger and more
diverse than existing TIR datasets. To understand the strengths
and weaknesses of a tracker on specic attributes, we dene 4
scenario attributes and 12 challenge attributes for attribute-based
evaluation. The training dataset contains 1,280 sequences with 47
object classes and over 650K bounding boxes, which is used for
learning TIR-specic deep features. In addition to the benchmark,
we evaluate more than 30 trackers on LSOTB-TIR, and provide
a detailed analysis. The results show that deep trackers achieve
promising performance. Moreover, we re-train several representa-
tive deep trackers using the proposed training dataset, and their
results on three benchmarks demonstrate that the proposed TIR
training dataset signicantly improves the performance of deep
TIR trackers.
The contributions of this paper are three-fold:
We propose a large-scale TIR object tracking benchmark,
LSOTB-TIR, with high-quality annotations using a self-designed
semi-automatic label tool. LSOTB-TIR is currently the largest
-200 -100 0 100
-200
-150
-100
-50
0
50
100
150 RGB based deep feature
person_003
person_004
person_013
-200 -100 0 100
-200
-150
-100
-50
0
50
100
150 TIR based deep feature
person_003
person_004
person_013
(a) Distribution of intra-class TIR objects
-200 -100 0 100
-200
-150
-100
-50
0
50
100
150 RGB based deep feature
cat_001
cow_001
dog_001
-200 -100 0 100
-200
-150
-100
-50
0
50
100
150 TIR based deep feature
cat_001
cow_001
dog_001
(b) Distribution of inter-class TIR objects
Figure 2: Comparison of the RGB based deep feature and
the TIR based deep feature using the t-SNE visualized
method [42]. The RGB and TIR based deep features are
extracted from the backbone network of two CFNets [52],
which are trained on an RGB dataset (VID [47]) and LSOTB-
TIR, respectively. We randomly choose 30 objects from each
sequence in LSOTB-TIR. (a) All objects belong to the person
class but come from dierent sequences. We can see that
the TIR based deep feature can recognize the dierences be-
tween intra-class TIR objects, which is curial to distinguish
distractors in TIR tracking. (b) All objects belong to dierent
classes. The TIR based deep feature can separate inter-class
objects more eectively than the RGB based deep feature.
Noting that each point in the gure denotes an object.
and most diverse TIR object tracking benchmark, which con-
sists of the training and evaluation datasets with the richest
object classes, scenarios, and challenges.
We conduct extensive evaluation experiments with more
than 30 trackers on LSOTB-TIR and provide a series of com-
parative analyses. The results show that deep trackers achieve
promising results and have the potential to obtain better per-
formance.
We re-train several representative deep trackers on LSOTB-
TIR, and their results on three benchmarks demonstrate that
the proposed training dataset signicantly improves the per-
formance of deep TIR trackers.
2 RELATED WORK
2.1 TIR trackers
In the past decade, some TIR object tracking algorithms have been
proposed to handle various challenges. These algorithms can be
Table 1: Comparison of the proposed LSOTB-TIR benchmark with other TIR object tracking benchmarks.
Benchmarks Num. of
sequences
Max
frames
Min
frames
Mean
frames
Total
frames
Frame
rates
Object
classes
Num. of
challenges
Scenario
attributes
Training
dataset
OSU [12] 6 2,031 601 1,424 8K 30 fps 1 n/a ✗ ✗
PDT-ATV [45] 8 775 77 486 4K 20 fps 3 n/a ✗ ✗
BU-TIV [58] 16 26,760 150 3,750 60K 30 fps 5 n/a ✗ ✗
LTIR [3] 20 1,451 71 563 11K 30 fps 6 6 ✗ ✗
VOT-TIR16 [17] 25 1,451 71 555 14K 30 fps 8 6 ✗ ✗
PTB-TIR [37] 60 1,451 50 502 30K 30 fps 1 9 ✗ ✗
RGB-T [31] 234 4,000 45 500 117K 30 fps 6 12 ✗ ✗
LSOTB-TIR (train.) 1,280 3,056 47 410 524K 30 fps 47 1 ✓ ✓
LSOTB-TIR (eval.) 120 2,110 105 684 82K 30 fps 22 12
roughly divided into two categories: conventional TIR trackers and
deep TIR trackers.
Conventional TIR trackers.
Conventional TIR object tracking
methods usually combine a conventional machine learning method
with a hand-crafted feature for handling various challenges. To
adapt the appearance variation of the object, Venkataraman et
al. [
53
] propose to online learn a robust intensity histogram based
appearance model using adaptive Kalman ltering, while TBOOST [
24
]
maintains a dynamic MOSSE lter [
7
] set using a continuously
switching mechanism according to appearance variation. Demir
et al. [
13
] use a part-based matching method, which integrates
the co-dierence feature of multiple parts to overcome the partial
deformation. To obtain more eective representations, DSLT [
60
]
combines gradient histograms with motion features and then is
used in a Structural Support Vector Machine (SSVM) [
26
] for TIR
object tracking. Observing that TIR images do not have color infor-
mation and lack sharp edges, Berg et al. [
4
] propose a distribution
eld representation [
48
] based matching algorithm for TIR object
tracking. Despite much progress, these trackers are limited by the
hand-crafted feature representation.
Deep TIR trackers.
Inspired by the success of deep learning in
visual tracking, several works introduce the Convolution Neural
Network (CNN) to improve the performance of TIR trackers. These
methods can be roughly divided into two categories, deep feature
based TIR trackers and matching based deep TIR trackers. Deep
feature based TIR trackers often use a pre-trained CNN for feature
extraction and then integrates the deep feature into conventional
tracking frameworks. For example, Gundogdu et al. [
23
] train a clas-
sication network on a small TIR dataset to extract the deep feature
of the TIR object and then combine it with the DSST tracker [
10
] for
TIR object tracking. MCFTS [
39
] combines multiple convolutional
features of VGGNet [
49
] with the Correlation Filters (CFs) [
27
] to
construct an ensemble TIR tracker. Gao et al. [
20
] combine deep
appearance features [
49
] and deep motion features [
22
] with SSVM
for TIR object tracking. ECO-stir [
61
] trains a Siamese network on
synthetic TIR images to extract TIR features and then integrates
them into the ECO [
9
] tracker. Li et al. [
33
] propose a mask sparse
representation deep appearance model with the particle lter frame-
work for TIR object tracking. Matching based deep TIR trackers
cast the tracking as a matching problem and usually o-line train a
matching network for online tracking. For example, Li et al. [
34
]
train a spatial variation aware matching network by introducing a
spatial attention mechanism for TIR object tracking. Liu et al. [
38
]
propose a multi-level similarity based matching network using a
semantic similarity module and a complementary structural simi-
larity module for TIR object tracking. However, most of these deep
models are learned from RGB images, which do not learn specic
patterns of TIR images and hence are less eective in representing
TIR objects.
2.2 TIR object tracking benchmarks
To evaluate TIR trackers, there are several widely used bench-
marks [
3
,
12
,
16
,
17
,
31
,
37
,
45
,
58
]. In the following, we introduce
these benchmarks briey.
OSU.
OSU [
12
] is a TIR and RGB image fusion dataset, which can be
used for TIR object tracking. This dataset contains only pedestrian
objects and all 6 videos are captured by a low-resolution TIR camera
in a static background.
PDT-ATV.
PDT-ATV [
45
] is a simulative aerial TIR object tracking
and detection dataset, which contains 8 sequences captured from a
low frame rate and low-resolution TIR camera. The dataset does
not have attribute labels and the tracking objects are dim and small.
BU-TIV.
BU-TIV [
58
] is used for several TIR visual tasks, includ-
ing object tracking, counting, and group motion estimation. This
dataset contains 16 sequences and more than 60K frames with a
high resolution.
LTIR.
LTIR [
3
] is the rst standard TIR object tracking benchmark
which contains 20 sequences with 6 object classes and an evalua-
tion toolkit. This benchmark is adopted by a TIR object tracking
competition, VOT-TIR15 [16].
VOT-TIR16.
VOT-TIR16 [
17
] is extended from VOT-TIR15. It con-
tains 25 sequences with 8 object classes and is more challenging
than VOT-TIR15. It has 6 challenge subsets that can be used to
evaluate a tracker on specic attributes.
PTB-TIR.
PTB-TIR [
37
] focuses on TIR pedestrian tracking, which
contains 60 sequences and 9 attribute challenges. The dataset is
collected from a variety of devices but lacks the division of dierent
scenario attributes.
RGB-T.
RGB-T [
31
] is a multi-modal tracking benchmark that con-
tains RGB and TIR videos on the same scene simultaneously. The
TIR videos are captured from a single low-resolution TIR camera
and can also be used for TIR object tracking.
(b) Hand-held scenario subset with 35 sequences and 16 object classes.
(c) Drone-mounted scenario subset with 25 sequences and 8 object classes.
(d) Vehicle-mounted scenario subset with 20 sequences and 4 object classes.
(e) Video surveillance scenario subset with 40 sequences and 8 object classes.
Figure 3: Examples of the proposed evaluation subset of LSOTB-TIR. It contains 4 non-overlapped scenario subsets with a total
of 120 sequences and 22 object classes.
Table 2: Denition of 4 scenarios and 12 challenges on LSOTB-TIR.
Scenario Denition Scenario Denition
VS The videos come from a surveillance camera. DS The videos are captured from a drone-mounted camera.
HH The videos are shotted from a hand-held camera. VM The videos come from a vehicle-mounted camera.
Challenge Denition Challenge Denition
TC Two same intensity targets cross each other. IV The target intensity is changed during tracking.
DIS Existing the intra-class object near the target. BC The background has a similar appearance to the target.
DEF The target is deformable during tracking. OCC The target is partly or fully occluded during tracking.
OV The target partly or fully leaves the image. SC The ratio of the target size is out of the range [0.5, 2].
FM The target moves more than 20 pixels. MB The target is blurry due to the target or camera motion.
LR The target size is lower than 800 pixels. ARV The target aspect ratio is out of the range [0.5, 2].
Although these benchmarks are widely used, they suer from
several problems, such as the small-scale, limited object classes, sce-
narios and challenges, and lack of training dataset. To solve these
issues, we present a large-scale and high-diversity TIR object track-
ing benchmark, LSOTB-TIR, consisting of an evaluation dataset and
a training dataset. The evaluation dataset contains 120 sequences
with more than 82K frames, 22 classes, 4 scenario subsets, and 12
challenge subsets, which is more diverse than these benchmarks.
The training dataset contains 1,280 sequences with more than 650K
bounding boxes and 47 object classes. Table 1 compares the pro-
posed benchmark with existing TIR object tracking benchmarks.
More comparisons with existing RGB tracking benchmarks are
shown in the supplementary material.
3 PROPOSED LSOTB-TIR BENCHMARK
In this section, we describe details of the proposed TIR object track-
ing benchmark, LSOTB-TIR. We rst introduce TIR videos collection
and processing in Section 3.1 and then we show how to annotate the
sequence in Section 3.2. Finally, we dene attributes of a sequence
in Section 3.3.
3.1 Data collection and processing
Our goal is to provide a large-scale and high-diversity general
TIR object tracking benchmark with the real-world scenarios and
challenges. To this end, we determine to track 5 kinds of moving
objects of interest (i.e., person, animal, vehicle, aircraft, and boat)
in 4 kinds of scenarios (i.e., hand-held, drone-mounted, vehicle-
mounted, and video surveillance, as shown in Table 2). Unlike
RGB object tracking, which is interested in arbitrary objects, TIR
object tracking is usually interested in objects with prominent
thermal radiation as the 5 categories mentioned above. The selected
5 categories of objects cover most of the targets of interest of TIR
object tracking in the civilian eld.
After determining the object classes and scenarios, we rst search
TIR videos on the Youtube website. Unfortunately, unlike RGB
videos, TIR videos are limited on Youtube. We try our best to obtain
600 TIR videos and each of them is within ten minutes. Since we
use TIR videos with the white-hot style for tracking, we lter out
some videos of other palette styles (e.g., iron, rainbow, and cyan).
We then convert the rest videos into image sequences and choose
the fragments according to the following principles. First, the object
must be active, which is caused by the movement of itself or the
camera. Second, the time that the object is fully occluded or out
of image range does not exceed one second. Third, the length of
the fragment does not exceed 4K frames. In addition, we choose
150 TIR sequences from existing datasets, such as BU-TIV [
58
], and
RGB-T [
31
]. Finally, we get 1,400 sequences that contain more than
600K frames and 47 object sub-classes of interest.
After getting all the sequences, we split them into a training
dataset and an evaluation dataset. We rst choose 200 sequences,
each of them contains at least one tracking challenging factor, for
evaluation. Then, we evaluate all the trackers using these sequences
and then select the most dicult 120 sequences as the nal eval-
uation subset according to the diculty degree of each sequence,
which is computed from the average success score of all the evalu-
ated trackers.
3.2 High-quality annotation
We rst decide an object of each frame contains 4 kinds of local
annotation information, including object class, position, occlusion,
and identity. We use a straight minimum bounding box to record
(a) Deformation (b) Scale variation
Figure 4: Examples of the bounding box adjustment. The yel-
low bounding boxes are generated by using a tracking algo-
rithm and the red bounding boxes are manually adjusted.
Figure 5: Attributes distribution of the evaluation subet of
LSOTB-TIR.
the position of an object. When an object is occluded above 50% or
out of the image above 50%, we dene this occlusion as true. This
attribute can be used to exclude the obvious noise and is useful for
training deep models.
Considering labeling is a time-consuming and labor-intensive
task, we design an auxiliary label tool (see the
supplementary
material
) based on the ECO-HC [
9
] tracker. This label tool helps
us generate a bounding box of the object in every frame semi-
automatically. When the tracker is set to track the object in a short
time (e.g., within 10 frames), the generated bounding boxes are
accurate in most situations. However, when the object undergoes
drastic appearance variation or scale change in a short time, the
generated bounding boxes of the label tool are not quite accurate.
For these bounding boxes, we adjust them manually, as shown in
Fig. 4. We suggest that the label tool makes the annotation more
accurate, smoother, and faster than the annotation in each frame
manually.
To complete the annotation, we assemble an annotation team
comprised of 8 Ph.D. students with careful training. To ensure the
quality of the annotations, we verify the annotations frame by frame
twice. Eventually, we get 1,400 carefully annotated sequences. Some
annotated sequences of the evaluation subset are shown in Fig. 3,
and some annotated sequences of the training subset are shown in
the supplementary material.
3.3 Attribute denition
In addition to the local attribute of each frame, we dene two kinds
of global attributes of a sequence in the evaluation dataset, namely
Table 3: Comparison of tracking results of more than 30 trackers on LSOTB-TIR. We rank these trackers according to their
success score. The property of a tracker includes feature representation (e.g., Deep: deep feature, HoG: histogram of gradient,
Cova: covariance feature, CN: color name, Raw: raw pixel), search strategy (e.g., DS: dense search, RS: random search, PF:
particle lter), category (e.g., D: discriminative, G: generative), and venue.
Tracker Performance Property
Success Precision Norm. Precision Speed Representation Search Category Venue
ECO-TIR (Ours) 0.631 0.768 0.695 18 fps Deep DS D
ECO-stir [61] 0.616 0.750 0.672 13 fps Deep DS D TIP19
ECO [9] 0.609 0.739 0.670 18 fps Deep DS D CVPR17
SiamRPN++ [30] 0.604 0.711 0.651 24 fps Deep DS D CVPR19
MDNet [44] 0.601 0.750 0.686 1 fps Deep RS D CVPR16
VITAL [51] 0.597 0.749 0.682 3 fps Deep RS D CVPR18
ATOM [8] 0.595 0.729 0.647 20 fps Deep RS D CVPR19
TADT [35] 0.587 0.710 0.635 40 fps Deep DS D CVPR19
SiamMask [56] 0.579 0.705 0.637 44 fps Deep DS D CVPR19
ECO-HC [9] 0.561 0.690 0.627 27 fps HoG DS D CVPR17
SiamFC-TIR (Ours) 0.554 0.700 0.626 45 fps Deep DS D
BACF [28] 0.535 0.648 0.591 26 fps HoG DS D ICCV17
SRDCF [11] 0.529 0.642 0.574 11 fps HoG DS D ICCV15
UDT [54] 0.523 0.629 0.575 35 fps Deep DS D CVPR19
MCCT [55] 0.522 0.634 0.574 27 fps HoG&CN DS D CVPR18
SiamFC [6] 0.517 0.651 0.587 45 fps Deep DS D ECCVW16
SiamFC-tri [14] 0.513 0.649 0.583 40 fps Deep DS D ECCV18
CREST [50] 0.504 0.597 0.544 2 fps Deep DS D ICCV17
Staple [5] 0.492 0.606 0.548 12 fps HoG&CN DS D CVPR16
MCFTS [39] 0.479 0.635 0.546 4 fps Deep DS D KBS17
CFNet-TIR (Ours) 0.478 0.580 0.540 24 fps Deep DS D
DSST [10] 0.477 0.555 0.505 50 fps HoG DS D BMVC14
MLSSNet [38] 0.459 0.596 0.549 25 fps Deep DS D arXiv19
CFNet [52] 0.416 0.519 0.481 24 fps Deep DS D CVPR17
HSSNet [34] 0.409 0.515 0.488 15 fps Deep DS D KBS19
HCF [40] 0.404 0.536 0.485 14 fps Deep DS D ICCV15
HDT [46] 0.403 0.538 0.478 6 fps Deep DS D CVPR16
TGPR [19] 0.403 0.514 0.495 1 fps Cova DS D ECCV14
RPT [36] 0.388 0.475 0.427 6 fps HoG PF D CVPR15
Struck [26] 0.384 0.477 0.432 17 fps Haar DS D TPAMI15
DSiam [25] 0.380 0.451 0.393 12 fps Deep DS D ICCV17
L1APG [2] 0.371 0.446 0.424 2 fps Raw PF G CVPR12
LCT [41] 0.364 0.471 0.430 27 fps HoG&Raw DS D CVPR15
ASLA [59] 0.338 0.429 0.393 3 fps Raw PF G CVPR12
KCF [27] 0.321 0.418 0.385 272 fps HoG DS D TPAMI15
MIL [1] 0.309 0.378 0.336 19 fps Haar DS D CVPR09
scenario and challenge. The corresponding attribute subsets can be
used to further evaluate a tracker on specic attributes. For the sce-
nario attribute, we dene 4 scenarios according to the TIR camera
platform, including video surveillance, drone-mounted, hand-held,
and vehicle-mounted, as shown in Table 2. These scenario subsets
can help us understand the strengths and weaknesses of a tracker
on specic application scenarios. For the challenge attribute, we
dene 12 challenges according to the real-world challenging factors
in TIR videos. For example, Thermal Crossover (TC) is dened as
that two TIR objects with the same intensity cross each other and
then lose their contour partly or fully. Distractor (DIS) is dened
as that the background near the target exists intra-class objects,
which disturbs the tracker to recognize the tracking target. This
challenge is a frequent and serious problem in TIR object tracking.
Intensity Variation (IV) is dened as that the intensity of the tar-
get is changed due to its temperature variation or the brightness
variation of the TIR camera. This challenge is an unfrequent issue
since the temperature of the target is stable in a short time. Some
other challenges, such as Background Clutter (BC), Deformation
(DEF), Occlusion (OCC), Out of View (OV), Scale Change (SC), Fast
Motion (FM), Motion Blur (MB), Low Resolution (LR), and Aspect
Ratio Variation (ARV), are dened in Table 2, and the distribution
of each challenge is shown in Fig. 5.
4 EXPERIMENTS
4.1 Evaluation criteria
We use two widely used evaluation criteria in visual tracking, i.e.,
Center Location Error (CLE) and Overlap Ratio (OR), as the base
metrics [
57
]. Base on these two metrics, precision, normalized pre-
cision, and success under One Pass Evaluation (OPE) are computed
to measure the overall performance of a tracker.
Precision.
CLE is the Euclidean distance between the center lo-
cation of the predicted position and the ground-truth. Precision
denotes the percentage of the successful frame whose CLE is within
a given threshold (e.g., 20 pixels).
Normalized precision.
Since the precision is sensitive to the reso-
lution of the image and the size of the bounding box, we normalize
the precision over the size of the ground-truth bounding box as
that in TrackingNet [
43
] and LaSOT [
15
]. We then use the Area
Under Curve (AUC) of the normalized precision between 0 and 0.5
to rank the trackers.
Success.
OR is the overlap rate between the predicted bounding
box and the ground-truth. Success denotes the percentage of the
successful frame whose OR is larger than a given threshold. We use
a dynamic threshold [0 1], and the corresponding AUC is used to
rank the tracking algorithms.
Speed.
We use the average frame rate of a tracker on the dataset
as the speed metric. We run all the trackers on the same PCs with
an i7 4.0GHZ CPU and a GTX-1080 GPU.
4.2 Overall performance evaluation
Evaluated trackers.
We choose publicly available 33 TIR and RGB
tracking methods for evaluation. These methods include sparse
trackers, such as L1APG [
2
], and ASLA [
59
]; correlation lter track-
ers, such as KCF [
27
], DSST [
10
], SRDCF [
11
], BACF [
28
], Sta-
ple [
5
], ECO-HC [
9
], and MCCT [
55
]; other hand-crafted feature
based trackers, including MIL [
1
], TGPR [
19
], LCT [
41
], Struck [
26
],
and RPT [
36
]; deep feature based correlation lter trackers, such
as HDT [
46
], ECO [
9
], HCF [
40
], DeepSTRCF [
32
], MCFTS [
39
],
CREST [
50
], and ECO-stir [
61
]; matching based deep trackers,
including SiamFC [
6
], CFNet [
52
], DSiam [
25
], SiamFC-tri [
14
],
TADT [
35
], SiamRPN++ [
30
], SiamMask [
56
], UDT [
54
], HSSNet [
34
],
and MLSSNet [
38
]; classication based deep trackers, such as MD-
Net [
44
], VITAL [
51
], and ATOM [
8
]. We do not change the param-
eters of these trackers provided by the authors in the experiment.
Furthermore, we re-train several deep trackers on the proposed TIR
training dataset for evaluation, such as ECO-TIR, SiamFC-TIR, and
CFNet-TIR. We use the backbone network of SiamFC-TIR as the
feature extractor in the ECO-TIR tracker.
Results and analysis.
Table 3 shows the overall performance and
property of all the evaluated trackers. Almost top 10 trackers are
the deep feature based methods. This shows that the deep feature
is superior to the hand-crafted feature and deep trackers achieve
promising performance in TIR object tracking. ECO-TIR obtains
the best success score (0.631) and precision score (0.768). While
ECO-stir [
61
] using the synthetic TIR based deep feature obtains
the second-best success score (0.616) and precision score (0.750).
Compared with ECO [
9
] which obtains the third-best success score
(0.609) using the pre-trained RGB based deep feature, ECO-TIR and
ECO-stir gain the success score by 2.2% and 0.7%, respectively. This
shows that the TIR based deep feature is superior to the RGB based
deep feature in TIR object tracking.
Matching based deep trackers, such as SiamRPN++ [
30
], TADT [
35
],
SiamMask [
56
], and SiamFC [
6
], achieve comparable performance
while running at a real-time speed. These trackers are usually o-
line trained to learn a matching network from a large-scale RGB
dataset end-to-end. Their favorable results demonstrate that the
RGB based deep feature models of these trackers can represent
TIR objects since there are some common patterns between RGB
objects and TIR objects. However, we argue that the RGB based
deep feature has less discriminative capacity in representing TIR
objects because the RGB based deep feature often tends to focus
on the texture feature [
21
]. Unlike RGB images, TIR images do not
have color information and lack rich texture features. We suggest
that the contour and structure features are critical for recognizing
TIR objects. For example, compared with SiamFC [
6
], our trained
SiamFC-TIR achieves a 3.7% success gain and a 3.9% normalized
precision gain. This demonstrates that the learned TIR based deep
feature has better discriminative capacity than the RGB based deep
feature for distinguishing TIR objects.
Classication based deep trackers, including MDNet [
44
], VI-
TAL [
51
], and ATOM [
8
], obtain favorable precision, which comes
from the powerful discriminative capacity of the online learned
binary classier using the positive and negative samples of the
tracked target. This is important for a tracker to adapt the appear-
ance variation of the tracked target. Unfortunately, online training
severely hampers their speed and easily leads to the over-tting
problem. However, for TIR trackers, we argue that online training
is critical for more robust tracking. Because the online training can
obtain a more powerful classier to recognize subtle dierences
between the tracked object and distractors.
4.3 Attribute-based evaluation
To understand the strengths and weaknesses of a tracker on spe-
cial attributes, we evaluate all the trackers on the dened attribute
subsets. Fig. 6(a) shows the success scores of the top 10 trackers
on the 4 scenario attribute subsets. We notice that the ranking of
these trackers is quite dierent between dierent scenario subsets.
For example, ECO-TIR is higher than MDNet [
44
] by 7.1% on the
surveillance subset, while it is lower than MDNet by 1.3% on the
drone subset. This shows that a tracker can not perform well on
all the scenarios. Furthermore, we nd that the diculty of the
vehicle-mounted scenario subset is the smallest and all the top
10 trackers achieve good performance. The major reason is that
vehicle-mounted scenario subset contains fewer challenges and the
limited tracked object classes. These scenario subsets can help us
develop specic-scenario based trackers to meet the requirements
of real-world applications. Fig. 6(b) shows the success scores of the
top 10 trackers on 4 challenge attribute subsets. MDNet gets the
best success score (0.612) on the thermal crossover subset, which
is higher than ECO-TIR by 10.5%. While ECO-TIR achieves the
best success scores (0.629 and 0.621) on the distractor and back-
ground clutter subsets, which are higher than MDNet by 4.6% and
2.7% respectively. This shows that a tracker can not handle all the
challenges. We attribute the good performance of ECO-TIR to the
learned TIR based deep feature. ATOM [
8
] obtains the best success
score on the scale variation and aspect ratio variation subsets. This
is because ATOM equips an overlap prediction network that can
obtain a more accurate bounding box of the target. More attribute-
based results are shown in the supplementary material.
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Success rate
Success plots - surveillance (40)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Success rate
Success plots - drone-mounted (25)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Success rate
Success plots - hand-held (35)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Success rate
Success plots - vehicle-mounted (20)
(a) Scenario attribute subset evaluation.
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Success rate
Success plots - thermal crossover (15)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Success rate
Success plots - distractor (70)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Success rate
Success plots - scale variation (47)
0 0.2 0.4 0.6 0.8 1
Overlap threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Success rate
Success plots - aspect ratio variation (28)
(b) Challenge attribute subset evaluation.
Figure 6: Attribute-based evaluation results. Only the top 10 trackers are shown for clarity.
Table 4: Comparison of 4 deep trackers trained on an RGB
dataset and the proposed TIR training dataset, respectively.
“-TIR" denotes this tracker is trained on the proposed TIR
training dataset.
Tracker VOT-TIR16 [17] PTB-TIR [37] LSOTB-TIR (Ours)
EAO Pre. Suc. Pre. Suc.
SiamFC [6] 0.225 0.623 0.480 0.651 0.517
SiamFC-TIR 0.250 0.758 0.566 0.700 0.554
CFNet [52] 0.254 0.629 0.449 0.519 0.416
CFNet-TIR 0.289 0.726 0.530 0.580 0.478
HSSNet [34] 0.262 0.689 0.468 0.515 0.409
HSSNet-TIR 0.271 0.723 0.490 0.566 0.435
ECO [9] 0.267 0.838 0.633 0.739 0.609
ECO-TIR 0.290 0.858 0.650 0.768 0.631
4.4 Training dataset validation
To validate that the proposed TIR training dataset can boost the
performance of deep TIR trackers, we re-train 4 representative
deep trackers using the proposed TIR training dataset. Then, we
compare them with the original trackers on 3 TIR object tracking
benchmarks, including VOT-TIR16 [
17
], PTB-TIR [
37
], and LSOTB-
TIR. Table 4 shows that all the re-trained deep trackers achieve
better performance. Despite CFNet [
52
] trained on a larger RGB
dataset (VID [
47
]), which is 4 times larger than the proposed TIR
training dataset, CFNet-TIR obtains an 8.1% success gain on PTB-
TIR and a 3.5% Expected Average Overlap (EAO [
29
]) gain on VOT-
TIR16. While SiamFC-TIR achieves a 13.5% precision gain on PTB-
TIR and a 3.7% success gain on LSOTB-TIR. ECO-TIR not only gets
the best performance on LSOTB-TIR but also obtains, compared
with ECO, a 2.3% EAO gain on VOT-TIR16 and a 1.7% success gain on
PTB-TIR. These results demonstrate that the proposed TIR training
dataset signicantly improves the performance of deep TIR trackers.
5 CONCLUSIONS
In this paper, we propose a large-scale and high-diversity general
thermal infrared object tracking benchmark with high-quality an-
notations, called LSOTB-TIR. To the best of our knowledge, LSOTB-
TIR is the largest and most diverse TIR object tracking benchmark
to date. By releasing LSOTB-TIR, we help the community develop
deep learning based TIR trackers and evaluate them fairly and com-
prehensively. We conduct extensive evaluation experiments with
more than 30 trackers on LSOTB-TIR. The results show that the
deep trackers achieve promising performance. Furthermore, we
re-train several deep trackers using the proposed training dataset,
and their results demonstrate that the proposed training dataset sig-
nicantly boosts the performance of deep TIR trackers. We suggest
that learning TIR-specic deep feature for improving TIR object
tracking is one of the main ways in the future.
6 ACKNOWLEDGMENT
This study is supported by the National Natural Science Foundation
of China (Grant No.61672183), by the Shenzhen Research Council
(Grant No.JCYJ2017041310455226946, JCYJ20170815113552036).
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Supplementary resource (1)

... YOT-78 also possesses 5 complex data which could be employed in the evaluation of a few important criteria as seen in the observed capturer. Moreover, LOTR [72], comprises greater along with also complex thermal infrared drone detection and monitoring, which has been developed in about 1400 thermal infrared series present in around 400k moving series. ...
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