Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network

Yosra Dorai, Frederic Chausse, Sami Gazzah, Najoua Essoukri Ben Amara

Abstract

The computer vision community has developed many multi-object tracking methods in various fields. The focus is put on traffic scenes and video-surveillance applications where tracking object features are challenging. Indeed, in these particular applications, objects can be partially or totally occluded and can appear differently. Usual detection methods generally fail to leverage those limitations. To deal with this, a framework for multi-object tracking based on the linking of tracklets (mini-trajectories) is proposed. Despite the number of errors (false positives or missing detections) made by the Faster R-CNN detector, short-term Faster R-CNN detection similarities are tracked. The goal is to get tracklets in a given number of frames. We suggest to associate tracklets and apply an update function to correct the trajectories. The experiments show that on the one hand, our approach outperforms the detector to find the undetected objects. And on the other hand, the developed method eliminates the false positives and shows the effectiveness of tracking.

References

  1. Ahuja, R. K., Magnanti, T. L., and Orlin, J. B. (1993). Network flows: theory, algorithms, and applications.
  2. Badie, J. (2015). Optimizing Process for Tracking People in video-camera network. PhD thesis, Universite Nice Sophia Antipolis.
  3. Badie, J. and Bremond, F. (2014). Global tracker: an online evaluation framework to improve tracking quality. In Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, pages 25-30. IEEE.
  4. Bae, S.-H. and Yoon, K.-J. (2014). Robust online multiobject tracking based on tracklet confidence and online discriminative appearance learning. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 1218-1225. IEEE.
  5. Bar-Shalom, Y., Fortmann, T., and Scheffe, M. (1980). Joint probabilistic data association for multiple targets in clutter. In Proc. Conf. on Information Sciences and Systems, pages 404-409.
  6. Battini, C. and Landi, G. (2015). 3d tracking based augmented reality for cultural heritage data management. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5):375.
  7. Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. (2011). Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE transactions on pattern analysis and machine intelligence, 33(9):1820-1833.
  8. Chong, C.-Y., Mori, S., Govaers, F., and Koch, W. (2014). Comparison of tracklet fusion and distributed kalman filter for track fusion. In Information Fusion (FUSION), 2014 17th International Conference on, pages 1-8. IEEE.
  9. Dehghan, A., Modiri Assari, S., and Shah, M. (2015). Gmmcp tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4091- 4099.
  10. Erdem, C. E., Sankur, B., and Tekalp, A. M. (2004). Performance measures for video object segmentation and tracking. IEEE Transactions on Image Processing, 13(7):937-951.
  11. Gidaris, S. and Komodakis, N. (2015). Object detection via a multi-region and semantic segmentation-aware cnn model. In Proceedings of the IEEE International Conference on Computer Vision, pages 1134-1142.
  12. Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, pages 1440-1448.
  13. Hadi, R. A., Sulong, G., and George, L. E. (2014). Vehicle detection and tracking techniques: a concise review. arXiv preprint arXiv:1410.5894.
  14. Kuo, C.-H., Huang, C., and Nevatia, R. (2010). Multi-target tracking by on-line learned discriminative appearance models. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 685-692. IEEE.
  15. Maamatou, H., Chateau, T., Gazzah, S., Goyat, Y., and Amara, N. E. B. (2015). Transfert d'apprentissage par un filtre séquentiel de monte carlo: application à la spécialisation d'un détecteur de piétons. In Journées francophones des jeunes chercheurs en vision par ordinateur.
  16. Mao, Y. and Yin, Z. (2015). Training a scene-specific pedestrian detector using tracklets. In 2015 IEEE Winter Conference on Applications of Computer Vision, pages 170-176. IEEE.
  17. Nillius, P., Sullivan, J., and Carlsson, S. (2006). Multitarget tracking-linking identities using bayesian network inference. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 2187-2194. IEEE.
  18. Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Li, H., Yang, S., Wang, Z., Loy, C.-C., et al. (2015). Deepid-net: Deformable deep convolutional neural networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2403-2412.
  19. Poiesi, F., Mazzon, R., and Cavallaro, A. (2013). Multitarget tracking on confidence maps: An application to people tracking. Computer Vision and Image Understanding, 117(10):1257-1272.
  20. Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  21. Stauffer, C. (2003). Estimating tracking sources and sinks. In Computer Vision and Pattern Recognition Workshop, 2003. CVPRW'03. Conference on, volume 4, pages 35-35. IEEE.
  22. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9.
  23. Tran, Q.-V. (2016). Non-contact breath motion detection using the lucas-kanade algorithm.
  24. Xing, J., Ai, H., and Lao, S. (2009). Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1200-1207. IEEE.
  25. Yang, B. and Nevatia, R. (2012). Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 1918-1925. IEEE.
  26. Zamir, A. R., Dehghan, A., and Shah, M. (2012). Gmcptracker: Global multi-object tracking using generalized minimum clique graphs. In Computer VisionECCV 2012, pages 343-356. Springer.
Download


Paper Citation


in Harvard Style

Dorai Y., Chausse F., Gazzah S. and Essoukri Ben Amara N. (2017). Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 492-498. DOI: 10.5220/0006155204920498


in Bibtex Style

@conference{visapp17,
author={Yosra Dorai and Frederic Chausse and Sami Gazzah and Najoua Essoukri Ben Amara},
title={Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={492-498},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006155204920498},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network
SN - 978-989-758-227-1
AU - Dorai Y.
AU - Chausse F.
AU - Gazzah S.
AU - Essoukri Ben Amara N.
PY - 2017
SP - 492
EP - 498
DO - 10.5220/0006155204920498