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Data Association and Prediction for Tracking Multiple Objects


  • Maharaja Research Foundation, Maharaja Institute of Technology, Mandya - 571438, Karnataka, India
  • PET Research Foundation, PES College of Engineering, Mandya - 571401, Karnataka, India


Objectives: Tracking moving objects is essential for high level computer vision analysis, like object behavior interpretation or gait recognition etc. In this paper, a new method to track multiple moving objects in the surveillance video sequence is proposed. Methods/Statistical Analysis: Object tracking is done by extracting color moments feature from the segmented foreground object and associating individual objects in the successive frames using nearest neighbor classifier and Chi- Square dissimilarity measure. Further, object tracking during occlusion has been addressed by predicting the missing state of the occluded object by applying Lagrange’s polynomial extrapolation using the apriori knowledge. Findings: The proposed method is evaluated using several challenging sequences of the benchmark IEEE PETS, IEEE CHANGE DETECTION, and EPFL dataset. Further, comparative evaluation with contemporary methods has been carried out to corroborate the efficacy of the proposed method. Application/Improvements: This work focuses on devising a new method to track multiple moving objects using color moments and Lagrange’s extrapolation framework which works for a complex environment and in the presence of occlusions. Further, the shadow elimination task after the motion segmentation could be considered as an improvement step in this work which will aid in better tracking results.


Color Moments, Lagrange’s Extrapolation, Multiple Objects Tracking, Object Tracking, Video Surveillance.

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  • Yilmaz A, Javed O, Shah M. Object tracking: A survey. ACM Computer Surveys. 2006; 38(4):1–13.
  • Hu W, Tan T, Wang L, Maybank S. A survey of visual surveillance of object motion and behaviours. IEEE Transactions on Systems Man and Cybernetics, Part C: Applications and Reviews. 2004; 34(3):334–52.
  • Wang X. Intelligent multi-camera video surveillance: A review. Pattern Recognition Letters. 2012; 34(1):3–19.
  • Soto ADM, Regazzoni CS. An overview on bayesian tracking for video analytics. IEEE, Computer Vision and Image Understanding. 2010; 104(1):90–126.
  • Yang H, Shao L, Zheng F, Wang L, Song Z. Recent advances and trends in visual tracking: A review. Nerocomputing. 2011; 74(18):3823–31.
  • Li L, Huang W, Gu IYH, Tian Q. Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process. 2004; 13(1):1459–72.
  • Liem M, Gavrilla DM. Multi-person tracking with overlapping cameras in complex, dynamic environments. Proceedings of the British Machine Vision Conference (BMVC); 2009. p. 1–1.
  • Girisha R, Murali S. Some new methodologies to track humans in a single environment using single and multiple cameras, Doctoral Thesis, University of Mysore; 2010. p. 1–3.
  • Girisha R, Murali S. Tracking humans using novel optical flow algorithm for surveillance videos. Proceedings of the Fourth Annual ACM Bangalore Conference, Bangalore; 2011. p. 1–7.
  • Dallalazadeh E, Guru DS. Feature-based tracking approach for detection of moving vehicle in traffic videos. Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, U S A; 2010. p. 254–60.
  • Naraghi MG, Koohi M, Sarbazy H. Object tracking based on estimation mesh method. Indian Journal of Science and Technology. 2012; 5(1):1–4.
  • Nummiaro K, Koller-Meier E, Svoboda T, Roth D, Gool LV. Color-based object tracking in multi-camera environments. DAGM-LNCS. 2003; 2781:591–9.
  • Zhu G, Zeng Q, Wang C. Efficient edge-based object tracking. Pattern Recognition. 2006; 39(1):2223–26.
  • Wang C, Liu H, Gao Y. Secne-adaptive hierarchical data association for multiple objects tracking. IEEE Signal Processing Letters. 2014; 21(6):697–701.
  • Altaf A, Raeis A. Presenting an effective algorithm for tracking of moving object based on support vector machine. Indian Journal of Science and Technology. 2015 Aug; 8(17):1–7. DOI: 10.17485/ijst/2015/v8i17/70326
  • Martin A, Martinez A. On collaborative people detection and tracking in complex scenarios. Image and Vision Computing. 2012; 30(2–5):345–54.
  • Kristan M, Pers J, Kovacic S, Leonardis A. A local-motion-based probabilistic model for visual tracking. Pattern Recognition. 2009; 40(6):2160–8.
  • Gilbert A, Bowden B. Incremental, scalable tracking of objects inter camera. Computer Vision and Image Understanding. 2008; 111:43–58.
  • Haritaoglu I, Harwood D, Davis LS. Hydra: multiple people detection and tracking using silhouettes. International Conference on Image Analysis and Processing, Venice; 1999. p. 280–5.
  • Seo DW, Chae HU, Kim BW, Choi WH, Jo KH. Human tracking based on multiple view homography. Journal of Universal Computer Science. 2009; 15(13):2463–84.
  • Xi Z, Xu D, Song W, Zheng Y. Algorithm with dynamic weights for multiple object tracking in video sequence. Optik - International Journal for Light and Electron Optics. 2015; 126(20):2500–7.
  • Jahandide H, Mohamedpour K, Moghaddam KA. A hybrid motion and appearance prediction model for robust visual object tracking. Pattern Recognition Letters. 2012; 33(16):2192–7.
  • Chang FM, Lian FL, Chou CC. Integration of modified inverse observation model and multiple hypothesis tracking for detecting and tracking humans. IEEE Transactions on Automation Science and Engineering. 2016; 13(1):160–70.
  • Aleksandrovich KP, Vyacheslavovich SP, Olegovich SSS. Fast multi-object tracking-by-detection using tracker affinity matrix. Indian Journal of Science and Technology. 2016 Jul; 9(27):1–13. DOI: 10.17485/ijst/2016/v9i27/97696
  • Heili A, Chen C, Odobez J-M. Detection-based multi-human tracking using a CRF model. Proceedings of IEEE ICCV; 2011.
  • Babu RV, Perez P, Bouthemy P. Robust tracking with motion estimation and local Kernel-based color modeling. Image and Vision Computing. 2007; 25:1205–16.
  • Thome N, Merad D, Miguet S. Learning articulated appearance models for tracking humans: A spectral graph matching approach. Image Communication. 2008; 23(10):769–87.
  • Ali I, Dailey MN. Multiple human tracking in high-density crowds. Image and Vision Computing. 2012; 30(12):966–77.
  • Yamashita A, Ito Y, Kaneko T, Asama H. Human tracking with multiple cameras based on face detection and mean shift. Proceeding of IEEE International Conference on Robotics and Biometrics; 2011. p. 1664–71.
  • Chandrajit M, Girisha R, Vasudev T. Motion segmentation from surveillance video sequence using chi-square statistics. Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications. (ERCICA-14). 2016; 8(7).
  • Soille P. Morphological image analysis: Principles and applications. New York, Inc. Secaucus, NJ: USA; 1999. p. 173–4.
  • Afifi AJ, Ashour WM. Image retrieval based on content using color feature. ISRN Computer Graphics. 2012; 2012:11.
  • Pele O, Werman M. The quadratic-chi histogram distance family. Daniilidis K, Maragos P, Paragios N, editors. Computer Vision-ECCV’10, Springer-Verlag: Berlin Heidelberg; 2010. p. 749–62.
  • Lagrange Polynomial [Internet]. [Cited 2016 Jul 25]. Available from:
  • Brezinski C, Zalia MR. Extrapolation methods: Theory and practice, North Holland; 1991.
  • PETS: Performance Evaluation of Tracking and Surveillance [Internet]. [Cited 2006 Jun 18]. Available from:‎.
  • PETS: Performance Evaluation of Tracking and Surveillance [Internet]. [Cited 2006 Jun 18]. Available from:‎.
  • Goyette N, Jodoin PM, Porikli F, Konrad J, Ishwar P. Changedetection.Net: A new change detection benchmark dataset. Proceedings IEEE Workshop on Change Detection (CDW-2012) at CVPR-2012, Providence: RI; 2012 Jun. p. 1–8.
  • Berclaz J, Fleuret F, Türetken E, Fua P. Multiple object tracking using K-shortest paths optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(9):1806–19.
  • Fleuret F, Berclaz J, Lengagne R, Fua P. Multi-camera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2008; 30(2):267–82.


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