Reducing Response Time for Multimedia Event Processing using Domain Adaptation

Full text

Reducing Response Time for Multimedia Event Processing using
Domain Adaptation
Asra Aslam
Insight Centre for Data Analytics
NUI Galway, Ireland
asra.aslam@insight-centre.org
Edward Curry
Insight Centre for Data Analytics
NUI Galway, Ireland
edward.curry@insight-centre.org
ABSTRACT
The Internet of Multimedia Things (IoMT) is an emerging concept
due to the large amount of multimedia data produced by sensing
devices. Existing event-based systems mainly focus on scalar data,
and multimedia event-based solutions are domain-specic. Multiple
applications may require handling of numerous known/unknown
concepts which may belong to the same/dierent domains with an
unbounded vocabulary. Although deep neural network-based tech-
niques are eective for image recognition, the limitation of having
to train classiers for unseen concepts will lead to an increase in the
overall response-time for users. Since it is not practical to have all
trained classiers available, it is necessary to address the problem
of training of classiers on demand for unbounded vocabulary. By
exploiting transfer learning based techniques, evaluations showed
that the proposed framework can answer within
∼
0.01 min to
∼
30
min of response-time with accuracy ranges from 95.14% to 98.53%,
even when all subscriptions are new/unknown.
CCS CONCEPTS
•Information systems →
Multimedia streaming;
•Computing
methodologies →
Neural networks;
•Software and its engineer-
ing →Publish-subscribe / event-based architectures.
KEYWORDS
Domain Adaptation, Online Training, Internet of Multimedia Things,
Event-Based Systems, Multimedia Stream Processing, Object De-
tection, Transfer Learning, Smart Cities, Machine Learning
ACM Reference Format:
Asra Aslam and Edward Curry. 2020. Reducing Response Time for Multi-
media Event Processing using Domain Adaptation. In Proceedings of the
2020 International Conference on Multimedia Retrieval (ICMR ’20), June
8–11, 2020, Dublin, Ireland. ACM, New York, NY, USA, 5 pages. https:
//doi.org/10.1145/3372278.3390722
1 INTRODUCTION
Due to ever increasing shift of data towards multimedia, the inclu-
sion of “multimedia things” in the domain of Internet of Things
(IoT) is a crucial step for the emerging applications of smart cities
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https://doi.org/10.1145/3372278.3390722
[
2
,
3
,
24
,
34
,
42
]. Event processing systems [
12
,
14
] are designed to
process data streams (consisting of mostly scalar data excluding
multimedia data). In case of smart cities, multiple types of multi-
media applications may require handling of multiple subscriptions
belonging to multiple domains (like {car, bus, pedestrian, bike}
ϵ
trac management, {car, taxi, bike}
ϵ
parking management, {ball,
person}
ϵ
sports event management etc.). High performance re-
quirement of real-time systems can be accomplished using existing
image processing systems but they are designed only for specic
domains, have limited user expressibility, and cannot successfully
realize the goal of generalizable multimedia event processing due
to their bounded object detection capability.
In our previous work [
5
,
6
] we analyzed the problem of general-
ized multimedia event processing but recognized the requirement
of availability of trained classiers for unknown concepts/objects
within subscriptions (unbounded vocabulary [
52
]). The online train-
ing of classier on request of any new/unknown subscription is an
option to be explored, which will help either in switching (trans-
forming) from one classier to another (like bus
→
car) or in the
construction of completely new classier (like ball). Also, existing
DNN based techniques [
18
] are well-known for easy knowledge
transfers among domains [
16
,
30
,
46
] but focused either on im-
proving accuracy or testing time. They do not analyze the overall
response time of the process of transfer and its impact on accuracy.
In this work, we propose an adaptive multimedia event process-
ing model, that leverages transfer learning-based techniques for
domain adaptation to handle unknown/new subscription within an
acceptable time frame. An example of multimedia event processing
specically for the detection of objects is shown in Fig. 1. Main
contributions of this article include a denition of quality metric
“response-time” supporting “unknown subscriptions”, an adaptive
approach using online classier construction to support multiple
domain-based subscriptions, and an instantiation of a classier
learning model by transferring knowledge among classiers us-
ing ne-tuning and freezing layers of neural network-based object
detection models.
2 BACKGROUND WITH RELATED WORK
Very few multimedia event based architectures for Internet of Mul-
timedia Things (IoMT) proposed in recent works [
2
,
42
,
44
], focused
on scalability and multimodal big data. However augmenting IoT
systems with multimedia event based approaches is not straightfor-
ward, and still haven’t been combined yet as an end-to-end model.
2.1 Domain-Specic Event Recognition
Event recognition in multimedia is one of the popular areas of re-
search [
21
,
32
,
51
]. Trac recognition systems [
17
,
26
] are highly

Figure 1: Generalized Multimedia Event Processing Scenario
ecient in analyzing and predicting trac events. Detection of
interesting events in sports video [
7
,
33
] is also one of the common
event recognition problem. Similarly other applications like ood
detection, surveillance based systems, cultural events, and/or nat-
ural disasters, are also introduced in literature [
1
,
31
,
43
,
50
] with
medium to high precision and no possibility for domain adaptation.
It can be concluded that although these event recognition systems
achieve high performance, they have no support for large vocab-
ulary which limits their user interface, they also demonstrate the
need to merge event based systems with multimedia methods each
time the domain changes, and therefore do not support domain
adaptation by themselves.
2.2 Domain Adaptive Event Recognition
As existing approaches of processing multimedia data are domain-
specic, the research is moving towards the concept of transfer
of knowledge from one domain to another [
8
,
11
,
49
]. Domain
Adaptation is the ability to utilize the knowledge of old domains
to identify unknown domains. The model learns from the source
domain consisting of labeled data and from the target domain using
unlabeled/labeled data, and in most use-cases, data available in the
source domain is much more than the target domain [
35
]. Many
approaches [
9
,
16
,
30
,
46
] with supervised/unsupervised transfer
learning have been proposed for domain adaptation and are mainly
focused on generalization ability for increasing accuracy not the
overall response time. An event recognition in still images by trans-
ferring objects and scene representations has been proposed in
work [
48
], where the correlations of the concepts of object, scene,
and events have been investigated. Similarly, large scale domain
adaptation based approaches [
4
,
10
,
19
,
20
,
40
] are also introduced
particularly for the detection of objects and it is desirable to bring
their abilities to the core of multimedia event processing.
3 MULTIMEDIA EVENT PROCESSING
3.1 Problem Formulation
The problem is focused on minimizing the response time for the
processing of multimedia events in order to answer user queries
consisting of unknown subscriptions (unbounded vocabulary), us-
ing an adaptive classier construction approach while achieving
high accuracy. It is primarily based on following two dimensions
“Response-Time” and “Unknown Subscriptions”:
(i) Response-Time:
It can be dened as the dierence between
the arrival and notication time of subscription processed using
specic classiers. Challenges with response-time in multimedia
event processing system include the following two cases:
Case 1: Classier for subscription available
This case contains subscriptions (like car, dog, bus) which are previ-
ously known to the multimedia event processing system, and their
classiers are already present in the model. Here response-time will
depend only on the testing time while excluding training time.
Case 2: Classier for subscription not available
This scenario includes subscriptions (like person, truck, trac_light)
for which classiers are not available and unknown to the system.
However by using the similarity of new subscriptions with existing
base classiers, we can further classify the present case as:
(a) Subscriptions require classiers similar to base classiers: Consider
an example of an unknown subscription “truck”, classier for truck
can be constructed from existing “bus” classier. Hence domain
adaptation time contributes to response-time.
(b) Subscriptions require classiers completely dierent from base
classiers: In such scenario, we assume no base classiers are simi-
lar to incoming subscription and response-time must includes cost
of training from scratch.
(ii) Unknown Subscriptions:
This dimension concerns the ability
to recognize new subscriptions with the naming of objects that may
not belong to the limited vocabulary of system. The lack of support
for unbounded vocabularies is a bottleneck for emerging applica-
tions [52], which we are referring to as Unknown Subscriptions.
3.2 Adaptive Multimedia Event Processing
A functional model has been designed for the adaptive multimedia
event processing engine (shown in Fig. 2), consisting of various
models discussed below:
Event Matcher analyzes user subscriptions (such as bus,car,dog)
and image events, and is responsible for the detection of condi-
tions in image events as specied by user query and preparation of
notications that need to be forwarded to users.

Figure 2: Design for Adaptive Multimedia Event Processing
Training and Testing Decision Model designed to analyze available
classiers and take the testing and training decision accordingly. It
evaluates the relationship of existing classiers with new/unknown
subscription and chooses the transfer learning technique.
Classier Construction Model phase performs the training of clas-
siers for subscribed classes, and updates the classier in the shared
resources after allowed response-time. The two options of transfer
learning used for classier construction includes ne-tuning and
freezing layers. In the rst approach we are performing ne-tuning
on a pre-trained model (presently ImageNet [
13
]), which uses the
technique of back-propagation with labels for target domain until
validation loss starts to increase. In the second approach, we are
using this previously trained classier to instantiate the network
of another classier required for a similar subscription concept.
In this particular scenario, we are freezing the backbone (convo-
lutional and pooling layers) of the neural network and training
only top dense fully connected layers, where the frozen backbone
is not updated during back-propagation and only ne-tuned layers
are getting updated and retrained during the training of classier.
The decision of construction of a classier for “bus” either from
pre-trained models (by ne-tuning) or from “car” classier (by freez-
ing) is taken with the help of computation of a threshold based on
subscriptions relatedness (path operator of WordNet [36]).
In Training Data Construction model, if a subscriber subscribes for
a class which is not present in any smaller object detection datasets
(Pascal VOC [
15
], and Microsoft COCO [
28
]), then a classier can
be constructed by fetching data from datasets (ImageNet [
13
], and
OID [
23
]) of more classes using online tools like ImageNet-Utils
1
and OIDv4_ToolKit
2
. Another common approach of online training
data construction is to use engines like “Google Images” or “Bing
Image Search API” to search for class names and download images.
Feature Extraction of Multimedia Events is responsible for the
detection of objects in image events using current deep neural
network based object detection models and incorporating new
classiers. Here we utilize image classication models [
18
,
37
,
45
]
in backbone network of object-detection models.
Shared Resources component consist of existing image processing
modules and training datasets. We use You Only Look Once (YOLO),
1https://github.com/tzutalin/ImageNet_Utils
2https://github.com/EscVM/OIDv4_ToolKit
Single shot multibox detector (SSD),and Focal loss based Dense object
detection (RetinaNet) as object detection models [
27
,
29
,
38
,
39
]. We
have some base classiers trained o-line using established dataset
Pascal VOC [
15
], which are used in constructing more classiers
using domain adaptation.
4 EVALUATION
4.1 Performance With/Without Adaptation
The results of mean Average Precision (mAP) for response time
from 0 to 30 min are shown in Table 1. In the case of arrival of a
completely new subscription (Case 2b in Section–3.1), all models
are trained from scratch without use of any pre-trained model. Here,
RetinaNet performs higher (mAP
∼
0
.
21) than other models and
the SSD300 does not converge without a pre-trained model. The
second and third row indicate the performance of proposed model
by applying domain adaptation techniques of ne-tuning/freezing
(Case 2a in Section–3.1). The recorded frame rates on our resources
for YOLOv3, SSD300, and RetinaNet are 114 fps, 21 fps, and 12 fps
respectively, where fps represent the number of frames per second.
It can be concluded that domain adaptation via freezing layers
can provide acceptable performance (i.e. accuracy
∼
92
.
74% with
precision
∼
0
.
50 using YOLOv3 model) in such short training time
(30min) as compared to ne-tuning of pre-trained model, which
is crucial to know before taking the decision of choosing either
pre-trained model or nearest classier.
4.2 Empirical Analysis for Domain Shift
We analyzes Transfer Loss,Accuracy, and Distribution Discrepancy
metrics, for domain adaptation. The “transfer loss” has been evalu-
ated on four domain transfers (varying from closely related domains
to not related domains), depicted in Fig. 3a. The transfer achieved
by YOLOv3 is better than other object detection models in case of
football to cricket ball and laptop to mango domain transfers. Here,
the transfer loss only indicates how well the transfer works on
multiple domains, and lower values are desired. However, the best
transfer is achieved by RetinaNet model on the transfer of cat to
dog class. Similarly the single shot detection (SSD) model achieve
its best on transfer of car to bus. Interestingly, the values of transfer
loss using models (SSD and RetinaNet) on other domain transfers

Table 1: mean Average Precision (mAP) on Initial (α=
0
) and
Final (β=30min) Response-Time With/Without Adaptation
Training Method YOLOv3 SSD300 RetinaNet
α β α β α β
Training from Scratch
0.01 0.07 0.00 0.00
0.09 0.21
Fine-Tuning ImageNet
0.00 0.12 0.05 0.17
0.27 0.36
Freezing Similar Classier 0.15 0.50 0.14 0.16
0.17 0.17
are quit high, and lead us to evaluate accuracy on these domain
adaptations.
The accuracy achieved by object detection models on the same
classes of domain transfers, is shown in Fig. 3b. It can be clearly seen
that all object detection models are able to provide high accuracy
on applying transfer learning techniques, however the YOLOv3
achieve the best accuracy on all domain transfers.
In-order to realize the variation of approximate distance (i.e. Dis-
tribution Discrepancy) among dierent domains, we have trained
few binary classiers that can classify source-target pair of classes
like cat and dog,car and bus etc. It can be seen in the results (Fig. 3c),
that distribution discrepancy (lower is better) for YOLOv3 is rela-
tively smaller among most of the domain transfers than for other
object detection models, which suggests that YOLOv3 neural net-
work closes the cross-domain gap more eectively, which also
explains its better accuracy than other object detection models.
4.3 Evaluations on Known/Unknown Domains
As results of high performance and domain shifts are in favor of
YOLOv3 with freezing layer based transfer learning technique, we
have selected YOLOv3 as an object detection model for perform-
ing further experiments on the unknown subscriptions. Table 2
provides a comparison of average accuracy and response time of
Adaptive Multimedia Event Processing model with existing domain-
specic models by considering their best performance. It can be
observed that existing multimedia event recognition models are
designed only for the detection of specic objects and answer such
known subscriptions in low response time, while fails to process any
unknown subscription. An average response time of the approach
for known subscriptions depends only on testing time (
∼
0.01 min)
and accuracy (98.53%) of object detection model. However, response
time for an unknown subscription includes training (presently
∼
30
min) via domain adaptation and achieves the accuracy of 95.14%.
5 CONCLUSION AND FUTURE WORK
This paper analyzed the problem of processing multimedia events
(specically object detection), for known/unknown subscriptions/concepts,
while minimizing the response time. We proposed a multimedia
event processing model with domain adaptation by utilizing trans-
fer learning based techniques (ne-tuning and freezing), for the
online training of neural network based models. Experiments on
current models evaluated the performance in low response-time,
along with an empirical analysis for domain shift. The proposed
system can achieve accuracy ranges from 95
.
14% to 98
.
53% within
∼
0
.
01 min to
∼
30 min of response-time using YOLOv3 even when
subscriptions are unknown. In future work, it can be extended
(a) Transfer Loss
(b) Accuracy
(c) A-Distance
Figure 3: Analysis for Domain Shift
Table 2: Comparison of Proposed with Existing Model(s)
Approach Subscription Performance
Response Time Accuracy
Vehicle Detection Known 0.001 min 97.3%
for Trac [47] Unknown ∞0%
Firearm Detection Known 0.0001 min 94.00%
for Security [25] Unknown ∞0%
Stolen Object Known 0.0007 min 93.58%
Detection [41] Unknown ∞0%
Car Parking Vaca- Known 0.17 min 97.9%
ncy Detection [22] Unknown ∞0%
Adaptive Multimedia Known 0.01 min 98.53%
Event Processing Model
Unknown 29.99 min 95.14%
for unsupervised/semi-supervised learning to reduce the need of
labeled data for new subscriptions.
ACKNOWLEDGMENTS
This work was supported by Science Foundation Ireland under grant
SFI/12/RC/2289_P2. Titan Xp GPU used was donated by NVIDIA.

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