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Online multi-object tracking: multiple instance based target appearance model

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Abstract

The online target specific feature based state estimation method has proved its applicability in video-based multiple objects tracking. This paper proposes a multi-modal tracking approach by coupling a distance based tracker with an appearance based tracking method. This method is applicable for trajectory formation of multiple objects with complex random motion structure. Proximity measurement scheme is applied to introduce structural context information in tracking-by-detection framework. The multiple-instance framework is formulated to incorporate spatial-temporal information of a target, to select significant features and to establish the statistical correlation between a prior model of the target and its recent observation. The proposed approach improves tracking performance significantly by reducing the number of fragmented trajectories and ID switches. The quantitative, as well as qualitative performance of the proposed method, is evaluated on six benchmark video sequences with the challenging environment like random movement between objects and partial occlusion. The proposed approach performs better than other state-of-the-art methods used for multiple objects tracking.

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References

  1. Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632

    Article  Google Scholar 

  2. Badal T, Nain N, Ahmed M, Sharma V (2015) An adaptive codebook model for change detection with dynamic background. In: 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, pp 110–116

  3. Badal T, Nain N, Ahmed M (2017) Multi-object trajectory coupling using online target specific decision making. In: 2017 IEEE International Conference on Identity, security and behavior analysis (ISBA). IEEE, pp 1–6

  4. Bae SH, Yoon KJ (2014) Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1218–1225

  5. Benezeth Y, Jodoin PM, Emile B, Laurent H, Rosenberger C (2008) Review and evaluation of commonly-implemented background subtraction algorithms. In: 19th International Conference on Pattern Recognition, 2008. ICPR 2008. IEEE, pp 1–4

  6. 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

    Article  Google Scholar 

  7. Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2009) Robust tracking-by-detection using a detector confidence particle filter. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp 1515–1522

  8. Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2011) Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans Pattern Anal Mach Intell 33(9):1820–1833

    Article  Google Scholar 

  9. Brutzer S, Höferlin B, Heidemann G (2011) Evaluation of background subtraction techniques for video surveillance. In: 2011 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1937–1944

  10. Czyz J, Ristic B, Macq B (2005) A color-based particle filter for joint detection and tracking of multiple objects. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings (ICASSP’05), vol 2. IEEE, pp ii–217

  11. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on Computer vision and pattern recognition, CVPR 2005, vol 1. IEEE, pp 886–893

  12. Di Lascio R, Foggia P, Percannella G, Saggese A, Vento M (2013) A real time algorithm for people tracking using contextual reasoning. Comput Vis Image Underst 117(8):892–908

    Article  Google Scholar 

  13. Ellis A, Ferryman J (2010) Pets2010 and pets2009 evaluation of results using individual ground truthed single views. In: 2010 Seventh IEEE International Conference on Advanced video and signal based surveillance (AVSS). IEEE, pp 135–142

  14. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  15. Fortmann TE, Bar-Shalom Y, Scheffe M (1980) Multi-target tracking using joint probabilistic data association. In: 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes. IEEE, pp 807–812

  16. Fragkiadaki K, Shi J (2011) Detection free tracking: exploiting motion and topology for segmenting and tracking under entanglement. In: 2011 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 2073–2080

  17. Führ G, Jung CR (2014) Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras. Pattern Recogn Lett 39:11–20

    Article  Google Scholar 

  18. Gordon N, Ristic B, Arulampalam S (2004) Beyond the kalman filter: particle filters for tracking applications. Artech House, London, p 830

    MATH  Google Scholar 

  19. Jingjing L, Ying C, Cheng Z, Hua Y, Li Z (2016) Tracking using superpixel features. In: 2016 Eighth International Conference on Measuring technology and mechatronics automation (ICMTMA). IEEE, pp 878–881

  20. Julier SJ, Uhlmann JK (1997) New extension of the kalman filter to nonlinear systems. In: AeroSense’97, International Society for Optics and Photonics, pp 182–193

  21. Kuo CH, Nevatia R (2011) How does person identity recognition help multi-person tracking?. In: 2011 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1217–1224

  22. Leal-Taixé L, Fenzi M, Kuznetsova A, Rosenhahn B, Savarese S (2014) Learning an image-based motion context for multiple people tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3542–3549

  23. Leal-Taixé L, Milan A, Reid I, Roth S, Schindler K (2015) Motchallenge 2015: towards a benchmark for multi-target tracking. arXiv:150401942

  24. Liu G, Chen Z, Yeung HWF, Chung YY, Yeh WC (2016) A new weight adjusted particle swarm optimization for real-time multiple object tracking. In: International Conference on Neural Information Processing. Springer, pp 643–651

  25. Milan A, Roth S, Schindler K (2014) Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell 36(1):58–72

    Article  Google Scholar 

  26. Milan A, Leal-Taixé L, Schindler K, Reid I (2015) Joint tracking and segmentation of multiple targets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5397–5406

  27. 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

    Article  Google Scholar 

  28. Okuma K, Taleghani A, De Freitas N, Little JJ, Lowe DG (2004) A boosted particle filter: multitarget detection and tracking. In: European Conference on Computer Vision. Springer, pp 28–39

  29. Papadourakis V, Argyros A (2010) Multiple objects tracking in the presence of long-term occlusions. Comput Vis Image Underst 114(7):835–846

    Article  Google Scholar 

  30. 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). IEEE, pp 1201–1208

  31. Reid D (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):843–854

    Article  Google Scholar 

  32. Song YM, Jeon M (2016) Online multiple object tracking with the hierarchically adopted gm-phd filter using motion and appearance. In: IEEE International Conference on Consumer electronics-asia (ICCE-asia). IEEE, pp 1–4

  33. Song X, Cui J, Zha H, Zhao H (2008) Vision-based multiple interacting targets tracking via on-line supervised learning. In: European Conference on Computer Vision. Springer, pp 642–655

  34. Wang D, Lu H, Yang MH (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325

    Article  MathSciNet  MATH  Google Scholar 

  35. Wang S, Fowlkes CC (2016) Learning optimal parameters for multi-target tracking with contextual interactions. Int J Comput Vis:1–18

  36. Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2411–2418

  37. Xiang Y, Alahi A, Savarese S (2015) Learning to track: online multi-object tracking by decision making. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4705–4713

  38. Yang M, Jia Y (2016) Temporal dynamic appearance modeling for online multi-person tracking. Comput Vis Image Underst 153:16–28

    Article  Google Scholar 

  39. Yang B, Nevatia R (2012) Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In: 2012 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1918–1925

  40. Yoon JH, Yang MH, Lim J, Yoon KJ (2015) Bayesian multi-object tracking using motion context from multiple objects. In: 2015 IEEE Winter Conference on Applications of computer vision (WACV). IEEE, pp 33–40

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Authors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, India

    Tapas Badal, Neeta Nain & Mushtaq Ahmed

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  1. Tapas Badal
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  2. Neeta Nain
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  3. Mushtaq Ahmed
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Correspondence to Tapas Badal.

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Badal, T., Nain, N. & Ahmed, M. Online multi-object tracking: multiple instance based target appearance model. Multimed Tools Appl 77, 25199–25221 (2018). https://doi.org/10.1007/s11042-018-5781-3

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  • DOI: https://doi.org/10.1007/s11042-018-5781-3

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