Multi-Targets Tracking Based On Bipartite Graph Matching

Jinqin Zhong 1 , Jieqing Tan 2 , Yingying Li 1 , Lichuan Gu 3  and Guolong Chen 4
  • 1 School of Computer and Information, Hefei University of Technology, Hefei 230009 Anhui, China
  • 2 School of Mathematics, Hefei University of Technology, Hefei 230009 Anhui, China
  • 3 School of Computer and Information, Anhui Agriculture University, Hefei 230036 Anhui, China
  • 4 Suzhou University, Suzhou 234000 Anhui, China

Abstract

Multi-target tracking is a challenge due to the variable number of targets and the frequent interaction between targets in complex dynamic environments. This paper presents a multi-target tracking algorithm based on bipartite graph matching. Unlike previous approaches, the method proposed considers the target tracking as a bipartite graph matching problem where the nodes of the bipartite graph correspond to the targets in two neighboring frames, and the edges correspond to the degree of the similarity measure between the targets in different frames. Finding correspondence between the targets is formulated as a maximal matching problem which can be solved by the dynamic Hungarian algorithm. Then, merging and splitting of the targets detection is proposed, the candidate occlusion region is predicted according to the overlapping between the bounding boxes of the interacting targets to handle the mutual occlusion problem. The extensive experimental results show that the algorithm proposed can achieve good performance on dynamic target interactions compared to state-of-the-art methods.

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  • 1. Li, Zhang, Yuan Li, R. Nevatia. Global Data Association for Multi-Object Tracking Using Network Flows. - In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 2008, 1-8.

  • 2. Bibby, C., I. Reid. Real-Time Tracking of Multiple Occluding Targets Using Level Sets. - In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, 1307-1314.

  • 3. Huang, Chang, Yuan Li, R. Nevatia. Multiple Target Tracking by Learning-Based Hierarchical Association of Detection Responses. - IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 2013, No 4, 898-910.

  • 4. Andriyenko, A., K. Schindler. Multi-Target Tracking by Continuous Energy Minimization. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 1265-1272.

  • 5. Comaniciu, D., V. Ramesh, P. Meer. The Variable Bandwidth Mean Shift and Data- Driven Scale Selection. - In: IEEE International Conference on Computer Vision,Vancouver, BC, 2001, 438-445.

  • 6. Tomasi, C., T. Kanade. Detection and Tracking of Point Features. Technical Report CMUCS-91-132, Carnegie Mellon University, Pittsburgh, PA, 1991.

  • 7. Zhao, T., R. Nevatia. Tracking Multiple Humans in Complex Situation. - IEEE Transactions on Analysis and Machine Intelligence, Vol. 26, 2004, No 9, 1208-1221.

  • 8. Reid, D. An Algorithm for Tracking Multiple Targets. - IEEE Transactions on Automatic Control, Vol. 24, 1979, No 6, 8430-854.

  • 9. Barshalom, Y., T. Fortmann. Tracking and Data Association. San Diego, Academic Press,1988.

  • 10. Goodman, I. R., R. Mahler, H. T. Nguyen. Mathematics of Data Fusion. Norwell, MA, Kluwer Academic Press, 1997.

  • 11. Mahler, R. P. S., M. Lockheed. Multi Target Bayes Filter via First-Order Multi Target Moments. - IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, 2003, No 4, 1152-1178.

  • 12. Wu, Zheng, A. Thangali, S. Sclaroff, M. Betke. Coupling Detection and Data Association for Multiple Target Tracking. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012, 1948-1955.

  • 13. Vo, Ba-Ngu, Wing-Kin Ma. The Gaussian Mixture Probability Hypothesis Density Filter. - IEEE Transaction on Signal Processing, Vol. 54, 2006, No 11, 4091-4104.

  • 14. Wang, Y. D., J. K. Wu, A. A. Kassim, W. M. Huang. Date-Driven Probability Hypothesis Density Filter for Visual Tracking. - IEEE Transaction on Circuits and Systems for Video Technology, Vol. 18, 2008, No 8, 1085-1095.

  • 15. Caetano, T. S., J. J. Mcauley, Li Cheng et al. Learning Graph Matching. - IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, 2009, No 6, 1048-1058.

  • 16. Duchenne, O., F. Bach, Kweon In-So, J. Ponce. A Tensor-Based Algorithm for High- Order Graph Matching. - IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 33, 2011, No 12, 2383-2395.

  • 17. Taeg, Sang Cho, S. Avidan, W. T. Freeman. A Probabilistic Image Jigsaw Puzzle Solver. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 183-190.

  • 18. Yang, Xingwei, N. Adluru, L. J. Latechi. Particle Filter with State Permutations for Solving Image Jigsaw Puzzles. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 2873-2880.

  • 19. Grundmann, M., V. Dwatra, Han Mei, I. Essa. Efficient Hierarchical Graph-Based Video Segmentation. - In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, 2141-2148.

  • 20. Hu, Nan, R. M. Rustamov, L. Guibas. Graph Matching with Anchor Nodes: A Learning Approach. - In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, 2013, 2096-2104.

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