Online Multiple Object Tracking Using Single Object Tracker and Maximum Weight Clique Graph

Tracking multiple objects is a challenging task in time-critical video analysis systems. In the popular tracking-by-detection framework, the core problems of a tracker are the quality of the employed input detections and the effectiveness of the data association. Towards this end, we propose a multiple object tracking method which employs a single object tracker to improve the results of unreliable detection and data association simultaneously. Besides, we utilize maximum weight clique graph algorithm to handle the optimal assignment in an online mode. In our method, a robust single object tracker is used to connect previous tracked objects to tackle the current noise detection and improve the data association as a motion cue. Furthermore, we use person re-identification network to learn the historical appearances of the tracklets in order to promote the tracker’s identification ability. We conduct extensive experiments on the MOT benchmark to demonstrate the effectiveness of our tracker.

[1]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[2]  Michael Felsberg,et al.  ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  James M. Rehg,et al.  Multiple Hypothesis Tracking Revisited , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Afshin Dehghan,et al.  GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs , 2012, ECCV.

[5]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[7]  Wongun Choi,et al.  Deep Network Flow for Multi-object Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Kwangjin Yoon,et al.  Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering , 2018, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[9]  Silvio Savarese,et al.  Learning to Track: Online Multi-object Tracking by Decision Making , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Haibin Ling,et al.  Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[11]  Afshin Dehghan,et al.  GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Stefan Roth,et al.  MOT16: A Benchmark for Multi-Object Tracking , 2016, ArXiv.

[13]  Silvio Savarese,et al.  Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Volker Eiselein,et al.  High-Speed tracking-by-detection without using image information , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[15]  Jingdong Wang,et al.  Deeply-Learned Part-Aligned Representations for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Laura Leal-Taixé,et al.  Tracking Without Bells and Whistles , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[18]  Pascal Fua,et al.  Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Hua Yang,et al.  Online Multi-Object Tracking with Dual Matching Attention Networks , 2018, ECCV.

[20]  Seung-Hwan Bae,et al.  Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Fan Yang,et al.  Trajectory Factory: Tracklet Cleaving and Re-Connection by Deep Siamese Bi-GRU for Multiple Object Tracking , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[22]  Wei Wu,et al.  High Performance Visual Tracking with Siamese Region Proposal Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Yang Zhang,et al.  Enhancing Detection Model for Multiple Hypothesis Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[24]  James M. Rehg,et al.  Multi-object Tracking with Neural Gating Using Bilinear LSTM , 2018, ECCV.

[25]  Wongun Choi,et al.  Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Fabio Tozeto Ramos,et al.  Simple online and realtime tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[27]  Bernt Schiele,et al.  Multiple People Tracking by Lifted Multicut and Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Xiaogang Wang,et al.  Deep Continuous Conditional Random Fields With Asymmetric Inter-Object Constraints for Online Multi-Object Tracking , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

[30]  Nenghai Yu,et al.  Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).