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Multimedia Systems
Published in: Multimedia Systems 6/2020

06-08-2020 | Regular Paper

Online multi-object tracking using KCF-based single-object tracker with occlusion analysis

Authors: Honghong Yang, Sheng Gao, Xiaojun Wu, Yumei Zhang

Published in: Multimedia Systems | Issue 6/2020

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Abstract

Most state-of-the-art multiple-object tracking (MOT) methods adopt the tracking-by-detection (TBD) paradigm, which is a two-step procedure including the detection module and the tracking module. In these methods, the tracking performance heavily depends on initial detections and data association. In this paper, we present an online MOT method by introducing a single-object tracking (SOT) based on correlation filter. Our contributions lie in twofold: (a) we use the KCF-based SOT in learning of discriminative target appearance relying on handcrafted and deep features and (b) we employ the predicted result to refine the detection mistakes in a new way. Furthermore, we introduce normalize APCE score as an occlusion indicator of tracklet confidence, and build a candidate target hypotheses set to improve the association performance. Both approaches are found beneficial to eliminate the track errors caused by the inability of association algorithm. The experimental results, both qualitative and quantitative on three benchmark datasets, demonstrate that our tracking algorithm achieves comparable or even better results than competitor approaches.
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Literature
1.
Chenouard, N., Bloch, I., Olivo-Marin, J.C.: Multiple hypothesis tracking for cluttered biological image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2736–3750 (2013)CrossRef
2.
Rezatofighi, S.H., Milan, A., Zhang, Z., et al.: Joint probabilistic data association revisited. In: IEEE International Conference on Computer Vision (ICCV), pp. 3047–3055 (2015)
3.
Butt, A.A., Collins, R.T.: Multi-target tracking by lagrangian relaxation to min-cost network flow. In: IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1846–1853 (2013)
4.
Lu, W.-G., Zhou, Z.-P., Zhanf, L.-J., et al.: Multi-target tracking by non-linear motion patterns based on hierarchical network flows. Multimed. Syst. 24, 175–194 (2018)CrossRef
5.
Yang, H., Wen, J., Wu, X., et al.: An efficient edge artificial intelligence multi-pedestrian tracking method with rank constraint. In: IEEE Transactions on Industrial Informatics, pp. 1–11 (2019)
6.
He, L., Ray, N., Guan, Y., et al.: Fast large-scale spectral clustering via explicit feature mapping. IEEE Trans. Cybern. 49(3), 1058–1071 (2019)CrossRef
7.
Chu, Q., Ouyang, W., Li, H., et al.: Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism. In: IEEE International Conference on Computer Vision (ICCV), pp. 4836–4845 (2017)
8.
Brendel, W., Amer, M., Todorovic, S.: Multiobject tracking as maximum weight independent set. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1273–1280 (2011)
9.
Dehghan, A., Assari, S.M., Shah, M.: GMMCP tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4091–4099 (2015)
10.
Russell, S., Oh, S., et al.: Markov chain monte carlo data association for multiple-target tracking. IEEE Trans. Autom. Control 54(3), 481–497 (2008)MATH
11.
Huang, C., Wu, B., Nevatia. R.: Robust object tracking by hierarchical association of detection responses. In: The European Conference on Computer Vision(ECCV), pp. 1–15 (2008)
12.
Xiang, Y., Alahi, A., Savarese, S.: Learning to track: online multiobject tracking by decision making. In: IEEE International Conference on Computer Vision (CVPR), pp. 4705–4713 (2015)
13.
Feng, W., Hu, Z., Wu, W., et al.: Multi-object tracking with multiple cues and switcher-aware classification. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–10 (2019)
14.
He, L., Zhang, H.: Kernel K-means sampling for nyström approximation. IEEE Trans. Image Process. 27(5), 2108–2120 (2018)MathSciNetCrossRef
15.
Joy, E., Peter, D.: Visual tracking with conditionally adaptive multiple template update scheme for intricate videos. Multimed. Syst. 24, 175–194 (2018)CrossRef
16.
Basar, T.A.: New approach to linear filtering and prediction problems. In: The Control Theory: Twenty-five Seminal Papers, pp. 35 (2009)
17.
Mahalanobis, A., Kumar, B.V., Casasent, D.: Minimum average correlation energy filters. Appl. Opt. 26(17), 3633–3640 (1987)CrossRef
18.
Kumar, B.V., Mahalanobis, A., Song, S., Sims, S.R., Epperson, J.F., et al.: Minimum squared error synthetic discriminant functions. Opt. Eng. 31(5), 915–922 (1992)CrossRef
19.
Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20–25 (2009)
20.
Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13–18 (2010)
21.
Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)CrossRef
22.
Ding, G., Chen, W., Zhao, S., et al.: Real-time scalable visual tracking via quadrangle kernelized correlation filters. IEEE Trans. Intell. Transport. Syst. 19, 140–150 (2017)CrossRef
23.
Danelljan, M., Khan, F.S., Felsberg, M., et al.: Adaptive color attributes for real-time visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1090–1097 (2014)
24.
Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
25.
Bae. S.H., Yoon, K.J.: Robust Online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1218–1225 (2018)
26.
Leal-Taix, L., Milan, A., Reid, I., et al.: Motchallenge 2015: towards a benchmark for multi-target tracking. arXiv: 150401942 (2015)
27.
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008(1), 246309 (2008)
28.
Yuan, L., Chang, H., Nevatia, R.: Learning to associate: hybridboosted multi-target tracker for crowded scene. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2953–2960 (2009)
29.
Leal-Taix, L., Milan, A., Schindler, K., et al.: Tracking the Trackers: an Analysis of the State of the Art in Multiple Object Tracking. arXiv:170402781 (2017)
30.
Leal-Taix, L., Canton-Ferrer, C., Schindler, K.: Learning by tracking: siamese CNN for robust target association. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 418–425 (2016)
31.
Bing, W., Li, W., Bing, S., et al.: Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. In: IEEE Conference on Computer Vision & Pattern Recognition Workshops (CVPRW), pp. 386–393 (2017)
32.
Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3029–3037 (2015)
33.
Milan, A., Schindler, K., Roth, S.: Multi-Target tracking by discrete-continuous energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2054–2068 (2016)CrossRef
34.
Maksai, A., Fua, P.: Eliminating exposure bias and loss-evaluation mismatch in multiple object tracking. arXiv:1811.10984v1 (2018)
35.
Chen, L., Peng, X., Ren, M.: Recurrent metric networks and batch multiple hypothesis for multi-object tracking. IEEE Access 7, 3093–3105 (2019)CrossRef
36.
Son, J., Baek, M., Cho, M., et al.: Multi-object Tracking with Quadruplet Convolutional Neural Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5620–5629 (2017)
37.
Milan, A., Rezatofighi, S.H., Dick, A., et al.: Online multi-target tracking using recurrent neural networks. In: The Thirty-First AAAI Conference on Artificial Intelligence, pp. 1–9 (2017)
38.
Kieritz, H., Becker, S., Hubner, W., et al.: Online multi-person tracking using Integral Channel Features. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 122–130 (2016)
39.
Hong, Y.J., Lee C.-R., Yang M.-H., et al.: Online multi-object tracking via structural constraint event aggregation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1392–1400 (2016)
40.
Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: Online multi-target tracking with strong and weak detections. In: The European Conference on Computer Vision(ECCV), pp. 1–11 (2016)
41.
Zhou, H., Ouyang, W., Cheng, J., et al.: Deep continuous conditional random fields with asymmetric inter-object constraints for online multi-object tracking. IEEE Trans. Circuits Syst. Video Technol. 29(4), 1011–1022 (2018)CrossRef
42.
Bae, S., Yoon, K.: Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 595–610 (2017)CrossRef
43.
Sadeghian, A., Alahi, A., Savarese, S., et al.: Tracking the untrackable: learning to track multiple cues with long-term dependencies. In: International Conference on Computer Vision(ICCV), pp. 300–311 (2017)
44.
Fang, K., Xiang, Y., Li, X., et al.: Recurrent autoregressive networks for online multi-object tracking. In: IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 466–475 (2018)
45.
Yoon, K., Kim, D., Yoon, Y.C., et al.: Data association for multi-object tracking via deep neural networks. Sensors 19(3), 559 (2019)CrossRef
46.
Xiang, J., Zhang, G., Hou, J., et al.: Online multi-object tracking based on feature representation and bayesian filtering within a deep learning Architecture. IEEE Access 7, 27923–27935 (2019)CrossRef
47.
Chu, P., Fan, H., Tan, C.C., et al.: Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In: Workshop on Applications of Computer Vision, pp. 161–170 (2019)
48.
Chen, J., Sheng, H., Zhang, Y., et al.: Enhancing detection model for multiple hypothesis tracking. In: Computer Vision and Pattern Recognition, pp. 2143–2152 (2017)
49.
Kim, C., Li, F., Rehg, J.M., et al.: Multi-object tracking with neural gating using bilinear LSTM. In: European conference on computer vision, pp. 208–224 (2018)
50.
Babaee, M., Li, Z., Rigoll, G., et al.: Occlusion handling in tracking multiple people using RNN. In: International Conference on IMAGE Processing, pp. 2715–2719 (2018)
51.
Wen, L., Du, D., Li, S., Bian, X., Lyu, S.: Learning Non-Uniform Hypergraph for Multi-Object Tracking. Comput. Vis. Pattern Recogn. 33, 8981–8988 (2019)
52.
Henschel, R., Lealtaixe, L., Cremers, D., et al.: Fusion of Head and Full-Body Detectors for Multi-object Tracking. In: Computer Vision and Pattern Recognition, pp. 1428–1437 (2018)
53.
Levinkov, E., Uhrig, J., Tang, S., et al.: Joint graph decomposition and node labeling: problem, algorithms, applications. In: computer vision and pattern recognition, pp. 1904–1912 (2017)
54.
Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., Xie, X.: Trajectory factory: Tracklet cleaving and reconnection by deep siamese bi-gru for multiple object tracking. In IEEE International Conference on Multimedia and Expo, ICME, pp. 1–6 (2018)
55.
Tang, S., Andriluka, M., Andres, B., Schiele, B.: Multiple people tracking by lifted multicut and person reidentification. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 3539–3548 (2017)
56.
Fu, Z., Angelini, F., Chambers, J., Naqvi, S.: Multi-level cooperative fusion of gm-phd filters for online multiple human tracking. IEEE Trans. Multimed. 21(9), 2277–2291 (2019)CrossRef
57.
Chu, Q., Ouyang W., Li, H. Wang, X., Liu, B., Yu, N.: Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism. In: The IEEE International Conference on Computer Vision (ICCV) pp. 4846–4855 (2018)
58.
Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.-H.: Online multi-object tracking with dual matching attention networks. In: The European Conference on Computer Vision (ECCV), pp, 366–382 (2018)
59.
Chen, L., Ai, H., Zhuang, Z., Shang, C.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: The IEEE International Conference on Multimedia and Expo, ICME, pp. 1–6 (2018)
60.
Lan, L., Wang, X., Zhang, S., Tao, D., Gao, W., Huang, T.: Interacting tracklets for multi-object tracking. IEEE Trans. Image Process. 27(9), 4585–4597 (2018)MathSciNetCrossRef
61.
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: The IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, Lecce, Italy, pp. 1–6 (2017)
62.
Sheng, H., Chen, J., Zhang, Y., Ke, W., Xiong, Z., Yu, J.: Iterative multiple hypothesis tracking with tracklet-level association. CSVT 29, 3660–3672 (2018)
63.
Shi, X., Ling, H., Pang, Y., Hu, W., Chu, P., Xing, J.: Rank-1 tensor approximation for high-order association in multitarget tracking. IJCV 127, 1063–1083 (2019)CrossRef
64.
Keuper, M., Tang, S., Andres, B., et al.: Motion segmentation and multiple object tracking by correlation co-clustering. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 140–153 (2020)CrossRef
65.
Fu, Z., Feng, P., Angelini, F., et al.: Particle PHD filter based multiple human tracking using online group-structured dictionary learning. IEEE Access 6, 14764–14778 (2018)CrossRef
66.
Lee, S., Kim, M., Bae, S., et al.: Learning discriminative appearance models for online multi-object tracking with appearance discriminability measures. IEEE Access 6, 67316–67328 (2018)CrossRef
67.
Young-Chul, Y., Abhijeet, B., Young-Min, S.G., et al.: Online multiobject tracking with historical appearance matching and scene adaptive detection filtering. IEEE International Conference on Advanced Video and Signal-based Surveillance(AVSS) (2018)
68.
Long, C., Haizhou, A., Zijie, Z., Chong, S.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In IEEE International Conference on Multimedia and Expo, ICME, pp. 1–6 (2018)
69.
Xu, J., Cao, Y., Zhang, Z., et al.: Spatial-temporal relation networks for multi-object tracking. International Conference on Computer Vision, pp. 3988–3998 (2019)
70.
Chen, L., Ai, H., Shang, C., et al.: Online multi-object tracking with convolutional neural networks. International Conference on Image Processing, pp. 645–649 (2017)
71.
Gan, W., Wang, S., Lei, X., et al.: Online CNN-based multiple object tracking with enhanced model updates and identity association. Signal Proces. Image Commun. 66, 95–102 (2018)CrossRef
72.
Fu, Z., Angelini, F., Naqvi, S.M., et al.: GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching. In: International Conference On Acoustics, Speech, And Signal Processing, pp, 4299–4303 (2018)
73.
Lee, S., Kim, E.: Multiple object tracking via feature pyramid siamese networks. IEEE Access 7, 8181–8194 (2019)CrossRef
Metadata
Title
Online multi-object tracking using KCF-based single-object tracker with occlusion analysis
Authors
Honghong Yang
Sheng Gao
Xiaojun Wu
Yumei Zhang
Publication date
06-08-2020
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2020
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-020-00675-4

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