Multimedia Tools and Applications

, Volume 77, Issue 19, pp 25199–25221 | Cite as

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

  • Tapas Badal
  • Neeta Nain
  • Mushtaq Ahmed


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.


Visual tracking Appearance model Motion structure Sparse representation Occlusion handling Video analysis 


  1. 1.
    Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  2. 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 110–116Google Scholar
  3. 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 1–6Google Scholar
  4. 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 1218–1225Google Scholar
  5. 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 1–4Google Scholar
  6. 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):1806–1819CrossRefGoogle Scholar
  7. 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 1515–1522Google Scholar
  8. 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):1820–1833CrossRefGoogle Scholar
  9. 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 1937–1944Google Scholar
  10. 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–217Google Scholar
  11. 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 886–893Google Scholar
  12. 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):892–908CrossRefGoogle Scholar
  13. 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 135–142Google Scholar
  14. 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):1627–1645CrossRefGoogle Scholar
  15. 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 807–812Google Scholar
  16. 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 2073–2080Google Scholar
  17. 17.
    Führ G, Jung CR (2014) Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras. Pattern Recogn Lett 39:11–20CrossRefGoogle Scholar
  18. 18.
    Gordon N, Ristic B, Arulampalam S (2004) Beyond the kalman filter: particle filters for tracking applications. Artech House, London, p 830zbMATHGoogle Scholar
  19. 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 878–881Google Scholar
  20. 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 182–193Google Scholar
  21. 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 1217–1224Google Scholar
  22. 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 3542–3549Google Scholar
  23. 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. 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 643–651Google Scholar
  25. 25.
    Milan A, Roth S, Schindler K (2014) Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell 36(1):58–72CrossRefGoogle Scholar
  26. 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 5397–5406Google Scholar
  27. 27.
    Milan A, Schindler K, Roth S (2016) Multi-target tracking by discrete-continuous energy minimization. IEEE Trans Pattern Anal Mach Intell 38(10):2054–2068CrossRefGoogle Scholar
  28. 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 28–39Google Scholar
  29. 29.
    Papadourakis V, Argyros A (2010) Multiple objects tracking in the presence of long-term occlusions. Comput Vis Image Underst 114(7):835–846CrossRefGoogle Scholar
  30. 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 1201–1208Google Scholar
  31. 31.
    Reid D (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):843–854CrossRefGoogle Scholar
  32. 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 1–4Google Scholar
  33. 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 642–655Google Scholar
  34. 34.
    Wang D, Lu H, Yang MH (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Wang S, Fowlkes CC (2016) Learning optimal parameters for multi-target tracking with contextual interactions. Int J Comput Vis:1–18Google Scholar
  36. 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 2411–2418Google Scholar
  37. 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 4705–4713Google Scholar
  38. 38.
    Yang M, Jia Y (2016) Temporal dynamic appearance modeling for online multi-person tracking. Comput Vis Image Underst 153:16–28CrossRefGoogle Scholar
  39. 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 1918–1925Google Scholar
  40. 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 33–40Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Malaviya National Institute of TechnologyJaipurIndia

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