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2016 | OriginalPaper | Buchkapitel

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

verfasst von : Loïc Fagot-Bouquet, Romaric Audigier, Yoann Dhome, Frédéric Lerasle

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object Tracking, we propose to formulate the multi-frame data association step as an energy minimization problem, designing an energy that efficiently exploits sparse representations of all detections. Furthermore, we propose to use a structured sparsity-inducing norm to compute representations more suited to the tracking context. We perform extensive experiments to demonstrate the effectiveness of the proposed formulation, and evaluate our approach on two public authoritative benchmarks in order to compare it with several state-of-the-art methods.

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Vorheriges Kapitel A Continuous Optimization Approach for Efficient and Accurate Scene Flow

Nächstes Kapitel Gated Siamese Convolutional Neural Network Architecture for Human Re-identification

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Xiang, Y., Alahi, A., Savarese, S.: Learning to track: online multi-object tracking by decision making. In: ICCV (2015)

Yoon, J.H., Yang, M.H., Lim, J., Yoon, K.J.: Bayesian multi-object tracking using motion context from multiple objects. In: WACV (2015)

Fagot-Bouquet, L., Audigier, R., Dhome, Y., Lerasle, F.: Online multi-person tracking based on global sparse collaborative representations. In: ICIP (2015)

Bae, S.H., Yoon, K.J.: Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: CVPR (2014)

Wang, S., Fowlkes, C.C.: Learning optimal parameters for multi-target tracking. In: BMVC (2015)

McLaughlin, N., Del Rincon, J.M., Miller, P.: Enhancing linear programming with motion modeling for multi-target tracking. In: WACV (2015)

Leal-Taix, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: CVPR (2014)

Milan, A., Schindler, K., Roth, S.: Multi-target tracking by discrete-continuous energy minimization. TPAMI (2016). doi:10.1109/TPAMI.2015.2505309

Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. TPAMI 36(1), 58–72 (2014)CrossRef

10.

Dicle, C., Sznaier, M., Camps, O.: The way they move: tracking targets with similar appearance. In: ICCV (2013)

11.

Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3D traffic scene understanding from movable platforms. TPAMI 36(5), 1012–1025 (2014)CrossRef

12.

Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)

13.

Zamir, A.R., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 343–356. Springer, Heidelberg (2012)CrossRef

14.

Dehghan, A., Assari, S.M., Shah, M.: GMMCP-tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: CVPR (2015)

15.

Brendel, W., Amer, M.R., Todorovic, S.: Multiobject tracking as maximum weight independent set. In: CVPR (2011)

16.

Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: ICCV (2015)

17.

Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: Multiple hypothesis tracking revisited. In: ICCV (2015)

18.

Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A.R., Reid, I.D.: Joint probabilistic data association revisited. In: ICCV (2015)

19.

Milan, A., Leal-Taix, L., Schindler, K., Reid, I.: Joint tracking and segmentation of multiple targets. In: CVPR (2015)

20.

Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: CVPR (2011)

21.

Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. TPAMI 33(11), 2259–2272 (2011)CrossRef

22.

Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: CVPR (2012)

23.

Jia, X., Lu, H., Yang, M.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)

24.

Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)

25.

Hong, Z., Mei, X., Prokhorov, D., Tao, D.: Tracking via robust multi-task multi-view joint sparse representation. In: ICCV (2013)

26.

Zhang, S., Yao, H., Sun, X., Lu, X.: Sparse coding based visual tracking: review and experimental comparison. Pattern Recogn. 46(7), 1772–1788 (2013)CrossRef

27.

Fagot-Bouquet, L., Audigier, R., Dhome, Y., Lerasle, F.: Collaboration and spatialization for an efficient multi-person tracking via sparse representations. In: AVSS (2015)

28.

Naiel, M.A., Ahmad, M.O., Swamy, M.N.S., Wu, Y., Yang, M.: Online multi-person tracking via robust collaborative model. In: ICIP (2014)

29.

Oh, S., Russell, S.J., Sastry, S.: Markov chain Monte Carlo data association for multi-target tracking. Trans. Autom. Control 54(3), 481–497 (2009)CrossRefMathSciNet

30.

Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31(2), 210–227 (2009)CrossRef

31.

Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1(3), 123–231 (2013)

32.

Quattoni, A., Carreras, X., Collins, M., Darrell, T.: An efficient projection for l1, infinity regularization. In: ICML (2009)

33.

Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Optimization with sparsity-inducing penalties. Found. Trends Mach. Learn. 4(1), 1–106 (2012)CrossRefMATH

34.

Leal-Taixé, L., Milan, A., Reid, I., Roth, S., Schindler, K.: MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking. arXiv:1504.01942 [cs] (2015)

35.

Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: A Benchmark for Multi-Object Tracking. arXiv:1603.00831 [cs] (2016)

36.

Ess, A., Leibe, B., Gool, L.V.: Depth and appearance for mobile scene analysis. In: ICCV (2007)

37.

Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: CVPR (2010)

38.

Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)

39.

Ferryman, J., Shahrokni, A.: Pets 2009: dataset and challenge. In: Performance Evaluation of Tracking and Surveillance (PETS-Winter) (2009)

40.

Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: CVPR (2012)

41.

Benfold, B., Reid, I.: Guiding visual surveillance by tracking human attention. In: BMVC (2009)

42.

Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. TPAMI 36(8), 1532–1545 (2014)CrossRef

43.

Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32(9), 1627–1645 (2010)CrossRef

44.

Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008(1), 1–10 (2008). doi:10.1155/2008/246309 CrossRef

45.

Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011)CrossRef

46.

Bewley, A., Ott, L., Ramos, F., Upcroft, B.: ALExTRAC: affinity learning by exploring temporal reinforcement within association chains. In: ICRA (2016)

Titel: Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking
verfasst von: Loïc Fagot-Bouquet
Romaric Audigier
Yoann Dhome
Frédéric Lerasle
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46484-8_47

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