Multi-Targets Tracking Based On Bipartite Graph Matching

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, 1-8.10.1109/CVPR.2008.4587584Search in Google Scholar

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, 1307-1314.10.1109/CVPR.2010.5539818Search in Google Scholar

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, 898-910.10.1109/TPAMI.2012.15923428432Search in Google Scholar

4. Andriyenko, A., K. Schindler. Multi-Target Tracking by Continuous Energy Minimization. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 1265-1272.10.1109/CVPR.2011.5995311Search in Google Scholar

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.Search in Google Scholar

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

7. Zhao, T., R. Nevatia. Tracking Multiple Humans in Complex Situation. - IEEE Transactions on Analysis and Machine Intelligence, Vol. 26, 2004, No 9, 1208-1221.10.1109/TPAMI.2004.7315742895Search in Google Scholar

8. Reid, D. An Algorithm for Tracking Multiple Targets. - IEEE Transactions on Automatic Control, Vol. 24, 1979, No 6, 8430-854.10.1109/TAC.1979.1102177Search in Google Scholar

9. Barshalom, Y., T. Fortmann. Tracking and Data Association. San Diego, Academic Press,1988.Search in Google Scholar

10. Goodman, I. R., R. Mahler, H. T. Nguyen. Mathematics of Data Fusion. Norwell, MA, Kluwer Academic Press, 1997.10.1007/978-94-015-8929-1Search in Google Scholar

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, 1152-1178.10.1109/TAES.2003.1261119Search in Google Scholar

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, 1948-1955.10.1109/CVPR.2012.6247896Search in Google Scholar

13. Vo, Ba-Ngu, Wing-Kin Ma. The Gaussian Mixture Probability Hypothesis Density Filter. - IEEE Transaction on Signal Processing, Vol. 54, 2006, No 11, 4091-4104.10.1109/TSP.2006.881190Search in Google Scholar

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, 1085-1095.10.1109/TCSVT.2008.927105Search in Google Scholar

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, 1048-1058.10.1109/TPAMI.2009.2819372609Search in Google Scholar

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, 2383-2395.10.1109/TPAMI.2011.11021646677Search in Google Scholar

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.Search in Google Scholar

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, 2873-2880.10.1109/CVPR.2011.5995535508312327795660Search in Google Scholar

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, 2141-2148.10.1109/CVPR.2010.5539893Search in Google Scholar

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. Search in Google Scholar