Article

DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking

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Abstract

In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a unified convolutional network (ConvNet), named DASOTNet. The intuition behind integrating data association and SOT is that they can complement each other. Following Siamese network architecture, DASOTNet consists of the shared feature ConvNet, the data association branch and the SOT branch. Data association is treated as a special re-identification task and solved by learning discriminative features for different targets in the data association branch. To handle the problem that the computational cost of SOT grows intolerably as the number of tracked objects increases, we propose an efficient two-stage tracking method in the SOT branch, which utilizes the merits of correlation features and can simultaneously track all the existing targets within one forward propagation. With feature sharing and the interaction between them, data association branch and the SOT branch learn to better complement each other. Using a multi-task objective, the whole network can be trained end-to-end. Compared with state-of-the-art online MOT methods, our method is much faster while maintaining a comparable performance.

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... The video includes special tracking scenes such as variable scale, partial occlusion, short-term full occlusion and long-term full occlusion. The experiment results show that compared with some typical algorithms proposed in recent years, OsaMOT exhibits improved tracking performance and the ability to resist occlusion and scale change, and most of its evaluation indexes are superior to those of STAM [25], NOTA [26], STRN [27], BLSTM_MTP_O [28], KCF16 [29], PHD_LMP [48], DEEP_TAMA [49] and DASOT [50]. ...
... Also note that indexes MOT, Rcll, FN are improved obviously than other methods in Table 11. For example, we achieve 2.56 MOTA boosts than NOTA [26] in Table 9. 5.8 MOTA boosts than BLSTM_MTP_O [28] and 4.49 Rcll boosts than DASOT [50] is observed in Table 11. In addition, 7.95 Rcll improvements is observed than DASOT [50] in Table 10. ...
... For example, we achieve 2.56 MOTA boosts than NOTA [26] in Table 9. 5.8 MOTA boosts than BLSTM_MTP_O [28] and 4.49 Rcll boosts than DASOT [50] is observed in Table 11. In addition, 7.95 Rcll improvements is observed than DASOT [50] in Table 10. The other indexes, such as MT, FN, ML are also improved obviously in Tables 10 and 11. ...
Article
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Abstract Multi‐object tracking (MOT), which uses the context information of image sequences to locate, maintain identities and generate trajectories of multiple targets in each frame, is key technology in the field of computer vision. To address the problems of occlusion and scale variation in low‐viewpoint MOT, OsaMOT is proposed here. First, according to the global occlusion state of each frame, OsaMOT proposes the adaptive anti‐occlusion feature to enhance the awareness and adaptability for occlusion. At the same time, OsaMOT uses the cascade screening mechanism to reduce the “virtual new target” phenomenon due to the dramatic change in target features caused by scale variation and occlusion. Finally, considering that the occluded templates will affect the tracking performance, OsaMOT proposes an adaptive anti‐noise template update mechanism according to the partial occlusion state of the target, which improves the purity of the template library and further enhances the applicability to occlusion. The experimental results show that OsaMOT can weaken the influence of scale variation, partial occlusion, short‐term full occlusion and long‐term full occlusion in the low‐viewpoint tracking scenes. Most evaluation indexes of OsaMOT under low‐viewpoint tracking scenario are superior to those of some typical algorithms proposed in recent years, and the tracking robustness is improved.
... Using the other detections and the objects in the active set A (t−1) , we construct the bipartite graph G and obtain the optimal matching using the Hungarian algorithm. As a result, the bounding boxes of the objects C (t−1) 1 and C (t−1) 2 are determined to be q (t) 1 and q (t) 2 , respectively. Next, we verify that q (t) 3 is a new object and include it in the active set A (t) at frame t. ...
... Require: Detection results D (1) , · · · , D (T ) Ensure: Active sets A (1) , · · · , A (T ) 1: Initialize A (1) 2: for t = 2 to T do 3: ...
... Require: Detection results D (1) , · · · , D (T ) Ensure: Active sets A (1) , · · · , A (T ) 1: Initialize A (1) 2: for t = 2 to T do 3: ...
Article
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How to make an online tracking model effectively adapt to newly appearing objects and object disappearance as well as appearance variations of target objects from few examples is an essential issue in multiple object tracking (MOT). Learning target appearances from few examples is a few-shot classification problem, while identifications of newly appearing objects and object disappearance has the aspect of open-set classification. In this work, we regard online MOT as open-set few-show classification to address both learning from few examples (few-shot classification) and unknown classes such as new objects (open-set classification). Specifically, we develop an embedding neural network, called VOFNet, consisting of convolutional and recurrent parts, to perform open-set few-shot classification. The convolutional part constructs a feature from an example of a target object and the recurrent part determines a representative feature of a target object from few examples. Then VOFNet is trained to provide effective features for open-set few-shot classification. Finally, we develop an online multiple object tracker based on the combination of VOFNet and the bipartite matching. The proposed tracker achieves 49.2 multiple object tracking accuracy (MOTA) with 28.9 frames per second on MOT17 dataset, which shows a significantly better trade-off between the accuracy and the speed than the existing algorithms. For example, the proposed algorithm yields about 3.17 times faster speed with 0.99 times lower accuracy than recent existing MOT algorithm [1].
... At the same time, our approach surpasses the other methods, TrctrD [42], LSST [11], FAMNet [6], YOONKJ [45], STRN [41], MTDF [13] and DASOT [7]. These approaches range from 53.7% to 52.0% MOTA and only LSST performs better at IDF1 but shows low number of MT. ...
Preprint
Full-text available
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the "context" information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of our proposed framework is the use of the Sinkhorn algorithm for end-to-end learning of the associations among objects during model training. The network is trained to predict object associations by taking into account constraints specific to the MOT task. Experimental results demonstrate the efficacy of the proposed approach in achieving top performance on the MOT17 Challenge among state-of-the-art online approaches.
... Multiple Object Tracking (MOT) is an important area of research in computer vision and artificial intelligence (Milan et al. 2017;Chu et al. 2020;Huang and Zhou 2019) dating back to 1988 (Pylyshyn and Storm 1988). The trackingbydetection paradigm allows us to decompose MOT into two tasks. ...
Article
Object association, i.e., the identification of which observations correspond to the same object, is a central task for the area of multiple object tracking. Two prominent models capturing this task have been introduced in the literature: the Lifted Multicut model and the more recent Lifted Paths model. Here, we carry out a detailed complexity-theoretic study of the problems arising from these two models that is aimed at complementing previous empirical work on object association. We obtain a comprehensive complexity map for both models that takes into account natural restrictions to instances such as possible bounds on the number of frames, number of tracked objects and branching degree, as well as less explicit structural restrictions such as having bounded treewidth. Our results include new fixed-parameter and XP algorithms for the problems as well as hardness proofs which altogether indicate that the Lifted Paths problem exhibits a more favorable complexity behavior than Lifted Multicut.
... For example, [84] uses a correlation layer that learns the dense temporal relations given sequential feature maps. DASOT [163] integrates data association and SOT in a unified framework, in which dense correlation feature maps are estimated for the temporal association, built upon truncated ResNet-50 with feature pyramid network (FPN) [164]. In addition, [165] estimates both temporal correlation and multi-scale spatial correlation with dense feature maps. ...
Preprint
Full-text available
Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. With the advancement of deep neural networks and the increasing demand for intelligent video analysis, MOT has gained significantly increased interest in the computer vision community. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. Unlike other computer vision tasks, such as image classification, object detection, re-identification, and segmentation, embedding methods in MOT have large variations, and they have never been systematically analyzed and summarized. In this survey, we first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives, including patch-level embedding, single-frame embedding, cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding. We further summarize the existing widely used MOT datasets and analyze the advantages of existing state-of-the-art methods according to their embedding strategies. Finally, some critical yet under-investigated areas and future research directions are discussed.
... At the same time, our approach surpasses the other methods, TrctrD [46], LSST [12], FAMNet [7], YOONKJ [49], STRN [45], MTDF [14] and DASOT [8]. These approaches range from 53.7% to 52.0% MOTA and only LSST performs better at IDF1 but shows low number of MT. ...
Chapter
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Article
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Article
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Preprint
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Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.
Conference Paper
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region pro-posal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolu-tional features. For the very deep VGG-16 model [18], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. The code will be released.
Article
Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.
Article
We present a multi-cue metric learning framework to tackle the popular yet unsolved Multi-Object Tracking (MOT) problem. One of the key challenges of tracking methods is to effectively compute a similarity score that models multiple cues from the past such as object appearance, motion, or even interactions. This is particularly challenging when objects get occluded or share similar appearance properties with surrounding objects. To address this challenge, we cast the problem as a metric learning task that jointly reasons on multiple cues across time. Our framework learns to encode long-term temporal dependencies across multiple cues with a hierarchical Recurrent Neural Network. We demonstrate the strength of our approach by tracking multiple objects using their appearance, motion, and interactions. Our method outperforms previous works by a large margin on multiple publicly available datasets including the challenging MOT benchmark.
Article
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.
Conference Paper
To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
Article
We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets), for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20x faster than the Faster R-CNN counterpart. Code will be made publicly available.
Article
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Article
In this paper we presen algorithms for the solution of the general assignment and transportation problems. In Section 1, a statement of the algorithm for the assignment problem appears, along with a proof for the correctness of the algorithm. The remarks which constitute the proof are incorporated parenthetically into the statement of the algorithm. Following this appears a discussion of certain theoretical aspects of the problem. In Section 2, the algorithm is generalized to one for the transportation problem. The algorithm of that section is stated as concisely as possible, with theoretical remarks omitted. 1. THE ASSIGNMENT PROBLEM. The personnel-assignment problem is the problem of choosing an optimal assignment of n men to n jobs, assuming that numerical ratings are given for each man’s performance on each job. An optimal assignment is one which makes the sum of the men’s ratings for their assigned jobs a maximum. There are n! possible assignments (of which several may be optimal), so that it is physically impossible, except
We analyze the computational problem of multi-object tracking in video sequences. We formulate the problem using a cost function that requires estimating the number of tracks, as well as their birth and death states. We show that the global solution can be obtained with a greedy algorithm that sequentially instantiates tracks using shortest path computations on a flow network. Greedy algorithms allow one to embed pre-processing steps, such as nonmax suppression, within the tracking algorithm. Furthermore, we give a near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects and linear in the sequence length. Our algorithms are fast, simple, and scalable, allowing us to process dense input data. This results in state-of-the-art performance.
We propose a network flow based optimization method for data association needed for multiple object tracking. The maximum-a-posteriori (MAP) data association problem is mapped into a cost-flow network with a non-overlap constraint on trajectories. The optimal data association is found by a min-cost flow algorithm in the network. The network is augmented to include an explicit occlusion model(EOM) to track with long-term inter-object occlusions. A solution to the EOM-based network is found by an iterative approach built upon the original algorithm. Initialization and termination of trajectories and potential false observations are modeled by the formulation intrinsically. The method is efficient and does not require hypotheses pruning. Performance is compared with previous results on two public pedestrian datasets to show its improvement.
Conference Paper
We present a detection-based three-level hierarchical association approach to robustly track multiple objects in crowded environments from a single camera. At the low level, reliable tracklets (i.e. short tracks for further analysis) are generated by linking detection responses based on conservative affinity constraints. At the middle level, these tracklets are further associated to form longer tracklets based on more complex affinity measures. The association is formulated as a MAP problem and solved by the Hungarian algorithm. At the high level, entries, exits and scene occluders are estimated using the already computed tracklets, which are used to refine the final trajectories. This approach is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results show a great improvement in performance compared to previous methods.
Article
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
  • Y Jia
  • E Shelhamer
  • J Donahue
  • S Karayev
  • J Long
  • R Girshick
  • S Guadarrama
  • T Darrell
Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093.
  • A Milan
  • L Leal-Taixé
  • I Reid
  • S Roth
  • K Schindler
Milan, A.; Leal-Taixé, L.; Reid, I.; Roth, S.; and Schindler, K. 2016. Mot16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831.
Online multi-object tracking with dual matching attention networks
  • J Zhu
  • H Yang
  • N Liu
  • M Kim
  • W Zhang
  • M.-H Yang
Zhu, J.; Yang, H.; Liu, N.; Kim, M.; Zhang, W.; and Yang, M.-H. 2018. Online multi-object tracking with dual matching attention networks. In ECCV.