Learning to track at 100 FPS with deep regression networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9905 LNCS, Issue , 2016, Pages 749-765

  Held, David ^a   Thrun, Sebastian ^a   Savarese, Silvio ^a  

^a Stanford University   (United States)

Author keywords

Deep learning; Machine learning; Neural networks; Tracking

Indexed keywords

BENCHMARKING; COMPUTER VISION; DEEP LEARNING; DEEP NEURAL NETWORKS; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; MOTION TRACKING; NEURAL NETWORKS; SURFACE DISCHARGES;

FEED-FORWARD NETWORK; GENERIC RELATIONSHIP; IMPROVE PERFORMANCE; MACHINE LEARNING TECHNIQUES; OBJECT MOTION; OFF-LINE TRAINING; ONLINE TRAINING; REAL-TIME APPLICATION;

OBJECT TRACKING;

EID: 84990068889     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46448-0_45     Document Type: Conference Paper

References (39)

1. Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and peopledetection-by-tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVpPR 2008, pp. 1–8. IEEE (2008)
2. Ba, J., Mnih, V., Kavukcuoglu, K.: Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755 (2014)
3. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPPR 2009, pp. 983–990. IEEE (2009)
4. Bazzani, L., Larochelle, H., Murino, V., Ting, J.A., Freitas, N.D.: Learning attentional policies for tracking and recognition in video with deep networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 937–944 (2011)
5. Bo, L., Ren, X., Fox, D.: Multipath sparse coding using hierarchical matching pursuit. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 660–667. IEEE (2013)
6. Cehovin, L., Kristan, M., Leonardis, A.: Is my new tracker really better than yours? In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 540–547. IEEE (2014)
7. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 4310–4318 (2015)
8. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a largescale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPPR 2009, pp. 248–255. IEEE (2009)
9. Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. arXiv preprint arXiv:1411.4389 (2014)
10. Dosovitskiy, A., Fischery, P., Ilg, E., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T., et al.: FlowNet: learning optical flow with convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2758–2766. IEEE (2015)
11. Fan, J., Xu, W., Wu, Y., Gong, Y.: Human tracking using convolutional neural networks. IEEE Trans. Neural Netw. 21(10), 1610–1623 (2010)
12. Geiger, A.: Probabilistic models for 3D urban scene understanding from movable platforms. Ph.D. thesis, KIT (2013)
13. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587. IEEE (2014)
14. Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: MatchNet: unifying feature and metric learning for patch-based matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3279–3286 (2015)
15. Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 263–270. IEEE (2011)
16. Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of the 32nd International Conference on Machine Learning, 6–11 July 2015, Lille (2015)
17. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
18. Jin, J., Dundar, A., Bates, J., Farabet, C., Culurciello, E.: Tracking with deep neural networks. In: 2013 47th Annual Conference on Information Sciences and Systems (CISS), pp. 1–5. IEEE (2013)
19. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)
20. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Largescale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
21. Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., Vojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: The visual object tracking vot2015 challenge results. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, pp. 1–23 (2015)
22. Kristan, M., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 191–217. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16181-5_14
23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
24. Kuen, J., Lim, K.M., Lee, C.P.: Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle. Pattern Recognit. 48(10), 2964–2982 (2015)
25. Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (2015)
26. Li, H., Li, Y., Porikli, F.: DeepTrack: learning discriminative feature representations by convolutional neural networks for visual tracking. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)
27. Li, H., Li, Y., Porikli, F.: DeepTrack: learning discriminative feature representations online for robust visual tracking. arXiv preprint arXiv:1503.00072 (2015)
28. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
29. Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, pp. 2204–2212 (2014)
30. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. arXiv preprint arXiv:1510.07945 (2015)
31. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 1–42 (2014)
32. Smeulders, A.W., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)
33. Tao, R., Gavves, E., Smeulders, A.W.M.: Siamese instance search for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
34. Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3119–3127 (2015)
35. Wang, N., Li, S., Gupta, A., Yeung, D.Y.: Transferring rich feature hierarchies for robust visual tracking. arXiv preprint arXiv:1501.04587 (2015)
36. Wang, N., Shi, J., Yeung, D.Y., Jia, J.: Understanding and diagnosing visual tracking systems. arXiv preprint arXiv:1504.06055 (2015)
37. Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: Advances in Neural Information Processing Systems, pp. 809–817 (2013)
38. Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353–4361 (2015)
39. Zhang, K., Liu, Q., Wu, Y., Yang, M.H.: Robust visual tracking via convolutional networks. arXiv preprint arXiv:1501.04505 (2015)