Abstract
Receiving growing attention for its various applications during the last few years, multi-object tracking remains a complex and challenging problem. Conventional grid-based tracking method is an efficient and effective method to tackle multi-object tracking, whose performance can be further boosted by intuitively taking into account the appearance similarity information yet. Therefore, we introduce appearance similarity edge into the grid-based method, where a Siamese network is utilized to produce the proposed similarity edge. In addition, we build a grid model with hexagonal cells and propose an access region mechanism including accessible area definition and an automatic-generation approach for entrance/exit grids. Since our tracking framework follows ’tracking-by-detection’ paradigm, the corresponding detection information is available to be integrated into access region mechanism, which will facilitate appropriate grid modeling. We verify the proposed Siamese network based appearance edge and access region mechanism through the experiments on some popular datasets like PETS-09, KITTI.
Similar content being viewed by others
References
Andriyenko A, Schindler K (2010) Globally optimal multi-target tracking on a hexagonal lattice. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision – ECCV 2010. Springer, Berlin, pp 466–479
Bae SH, Yoon K (2014) Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: IEEE Conf. computer vision and pattern recognition, pp 1218–1225. https://doi.org/10.1109/CVPR.2014.159
Berclaz J, Fleuret F, Fua P (2009) Multiple object tracking using flow linear programming. In: 2009 Twelfth IEEE international workshop on performance evaluation of tracking and surveillance, pp 1–8. https://doi.org/10.1109/PETS-WINTER.2009.5399488
Berclaz J, Fleuret F, Turetken E, Fua P (2011) Multiple object tracking using k-shortest paths optimization. IEEE Trans Pattern Anal Mach Intell 33(9):1806–1819. https://doi.org/10.1109/TPAMI.2011.21
Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J Image Video Process 2008(1):246309. https://doi.org/10.1155/2008/246309
Bewley A, Ott L, Ramos F, Upcroft B (2016) Alextrac: affinity learning by exploring temporal reinforcement within association chains. In: 2016 IEEE International conference on robotics and automation (ICRA), pp 2212–2218. https://doi.org/10.1109/ICRA.2016.7487371
Chen L, Wang W, Panin G, Knoll A (2015) Hierarchical grid-based multi-people tracking-by-detection with global optimization. IEEE Trans Image Process 24(11):4197–4212. https://doi.org/10.1109/TIP.2015.2451013
Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 539–546. https://doi.org/10.1109/CVPR.2005.202
Chung D, Tahboub K, Delp EJ (2017) A two stream siamese convolutional neural network for person re-identification. In: 2017 IEEE International conference on computer vision (ICCV), pp 1992–2000. https://doi.org/10.1109/ICCV.2017.218
Dicle C, Camps OI, Sznaier M (2013) The way they move: tracking multiple targets with similar appearance. In: Proc. ICCV, pp 2304–2311. https://doi.org/10.1109/ICCV.2013.286
Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545. https://doi.org/10.1109/TPAMI.2014.2300479
Dosovitskiy A, Fischer P, Ilg E, Häusser P, Hazirbas C, Golkov V, vd Smagt P, Cremers D, Brox T (2015) Flownet: learning optical flow with convolutional networks. In: 2015 IEEE international conference on computer vision (ICCV), pp 2758–2766. https://doi.org/10.1109/ICCV.2015.316
Elfes A (1989) Using occupancy grids for mobile robot perception and navigation. Computer 22(6):46–57. https://doi.org/10.1109/2.30720
Ess A, Leibe B, Schindler K, Gool LV (2008) A mobile vision system for robust multi-person tracking. In: IEEE Conf. computer vision and pattern recognition, pp 1–8. https://doi.org/10.1109/CVPR.2008.4587581
Ess A, Schindler K, Gool LV (2009) Improved multi-person tracking with active occlusion handling. IEEE ICRA workshop on people detection & tracking
Ferryman J, Shahrokni A (2009) Pets2009: dataset and challenge. In: 2009 Twelfth IEEE international workshop on performance evaluation of tracking and surveillance, pp 1–6. https://doi.org/10.1109/PETS-WINTER.2009.5399556
Fleuret F, Berclaz J, Lengagne R, Fua P (2008) Multicamera people tracking with a probabilistic occupancy map. IEEE Trans Pattern Anal Mach Intell 30(2):267–282. https://doi.org/10.1109/TPAMI.2007.1174
Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The Kitti vision benchmark suite. In: Conference on computer vision and pattern recognition (CVPR)
Geiger A, Lauer M, Wojek C, Stiller C, Urtasun R (2014) 3d traffic scene understanding from movable platforms. IEEE Trans Pattern Anal Mach Intell 36(5):1012–1025. https://doi.org/10.1109/TPAMI.2013.185
He K, Gkioxari G, Dollar P, Girshick R (2017) Mask r-cnn. In: The IEEE International conference on computer vision (ICCV)
Ju J, Kim D, Ku B, Han DK, Ko H (2017) Online multi-person tracking with two-stage data association and online appearance model learning. IET Comput Vis 11(1):87–95. https://doi.org/10.1049/iet-cvi.2016.0068
Leal-Taixé L, Milan A, Reid I, Roth S, Schindler K (2015) MOTChallenge 2015: towards a benchmark for multi-target tracking. arXiv:1504.01942 [cs]
Liang X, Shen X, Xiang D, Feng J, Yan LLS (2016) Semantic object parsing with local-global long short-term memory. In: IEEE Conf. computer vision and pattern recognition, pp 3185–3193. https://doi.org/10.1109/CVPR.2016.347
Lu CW, Lin CY, Hsu CY, Weng MF, Kang LW, Liao HYM (2013) Identification and tracking of players sport videos. In: International conference on internet multimedia computing and service, pp 113–116
Milan A, Roth S, Schindler K (2014) Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell 36(1):58–72. https://doi.org/10.1109/TPAMI.2013.103
Milan A, Schindler K, Roth S (2016) Multi-target tracking by discrete-continuous energy minimization. IEEE Trans Pattern Anal Mach Intell 38 (10):2054–2068. https://doi.org/10.1109/TPAMI.2015.2505309
Milan A, Rezatofighi SH, Dick A, Reid I, Schindler K (2017) Online multi-target tracking using recurrent neural networks. In: Proc. AAAI
Nillius P, Sullivan J, Carlsson S (2006) Multi-target tracking - linking identities using Bayesian network inference. In: IEEE Conf. on computer vision and pattern recognition, vol 2, pp 2187–2194. https://doi.org/10.1109/CVPR.2006.198
Okuma K, Taleghani A, de Freitas N, Little JJ, Lowe DG (2004) A boosted particle filter: multitarget detection and tracking. Springer, Berlin, pp 28–39
Pirsiavash H, Ramanan D, Fowlkes CC (2011) Globally-optimal greedy algorithms for tracking a variable number of objects. In: 2011 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1201–1208. https://doi.org/10.1109/CVPR.2011.5995604
Reid D (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):843–854. https://doi.org/10.1109/TAC.1979.1102177
Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Schulter S, Vernaza P, Choi W, Chandraker M (2017) Deep network flow for multi-object tracking. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2730–2739. https://doi.org/10.1109/CVPR.2017.292
Shen F, Zhou X, Yang Y, Song J, Shen HT, Tao D (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25 (12):5610–5621. https://doi.org/10.1109/TIP.2016.2612883
Shen F, Yang Y, Liu L, Liu W, Tao D, Shen HT (2017) Asymmetric binary coding for image search. IEEE Trans Multimed 19(9):2022–2032. https://doi.org/10.1109/TMM.2017.2699863
Shen F, Xu Y, Liu L, Yang Y, Huang Z, Shen HT (2018) Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE Trans Pattern Anal Mach Intell 40(12):3034–3044. https://doi.org/10.1109/TPAMI.2018.2789887
Song Y, Jeon M (2016) Online multiple object tracking with the hierarchically adopted gm-phd filter using motion and appearance. In: 2016 IEEE International conference on consumer electronics-Asia (ICCE-Asia), pp 1–4. https://doi.org/10.1109/ICCE-Asia.2016.7804800
Sun S, Akhtar N, Song H, Mian A, Shah M (2018) Deep affinity network for multiple object tracking. arXiv:1810.11780
Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 1701–1708. https://doi.org/10.1109/CVPR.2014.220
Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision – ECCV 2016. Springer International Publishing, Cham, pp 791–808
Wang B, Wang G, Chan KL, Wang L (2014) Tracklet association with online target-specific metric learning. In: IEEE Conf. computer vision and pattern recognition, pp 1234–1241. https://doi.org/10.1109/CVPR.2014.161
Xing J, Ai H, Liu L, Lao S (2011) Multiple player tracking sports video: a dual-mode two-way Bayesian inference approach with progressive observation modeling. IEEE Trans Image Process 20(6):1652–1667. https://doi.org/10.1109/TIP.2010.2102045
Yang W, Li J, Zheng H, Xu RYD (2018) A nuclear norm based matrix regression based projections method for feature extraction. IEEE Access 6:7445–7451. https://doi.org/10.1109/ACCESS.2017.2784800
Yoon K, Kim DY, Young Chul Y, Jeon M (2019) Data association for multi-object tracking via deep neural networks. Sensors 19:559. https://doi.org/10.3390/s19030559
Yoon Y, Boragule A, Song Y, Yoon K, Jeon M (2018) Online multi-object tracking with historical appearance matching and scene adaptive detection filtering. In: 2018 15th IEEE International conference on advanced video and signal based surveillance (AVSS), pp 1–6. https://doi.org/10.1109/AVSS.2018.8639078
Young Chul Y, Song YM, Yoon K, Jeon M (2018) Online multi-object tracking using selective deep appearance matching. In: 2018 IEEE International conference on consumer electronics-Asia (ICCE-Asia), pp 206–212. https://doi.org/10.1109/ICCE-ASIA.2018.8552105
Zagoruyko S, Komodakis N (2015) Learning to compare image patches via convolutional neural networks. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 4353–4361. https://doi.org/10.1109/CVPR.2015.7299064
žbontar J, LeCun Y (2015) Computing the stereo matching cost with a convolutional neural network. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1592–1599. https://doi.org/10.1109/CVPR.2015.7298767
Zhang L, Li Y, Nevatia R (2008) Global data association for multi-object tracking using network flows. In: IEEE Conf. computer vision and pattern recognition, pp 1–8. https://doi.org/10.1109/CVPR.2008.4587584
Zheng W (2017) Multichannel eeg-based emotion recognition via group sparse canonical correlation analysis. IEEE Trans Cogn Develop Syst 9(3):281–290. https://doi.org/10.1109/TCDS.2016.2587290
Acknowledgements
This work is supported by the National Natural Science Foundation of China(Grant No.61727802).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, L., Lou, J., Xu, F. et al. Grid-based multi-object tracking with Siamese CNN based appearance edge and access region mechanism. Multimed Tools Appl 79, 35333–35351 (2020). https://doi.org/10.1007/s11042-019-07747-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-07747-2