Robust online visual tracking with a single convolutional neural network

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

Volumn 9007, Issue , 2015, Pages 194-209

  Li, Hanxi ^a,c   Li, Yi ^a,b   Porikli, Fatih ^a,b  

^a CSIRO   (Australia)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c JIANGXI NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; NEURAL NETWORKS; SAMPLING; SOCIAL NETWORKING (ONLINE); STOCHASTIC SYSTEMS; TARGET TRACKING; TRACKING (POSITION);

CONVOLUTIONAL NEURAL NETWORK; DEEP NEURAL NETWORKS; FEATURE LEARNING; FEATURE REPRESENTATION; ON-LINE TRACKING; SELECTION MECHANISM; STOCHASTIC GRADIENT DESCENT; TRACKING ACCURACY;

COMPUTER VISION;

EID: 84929610418     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16814-2_13     Document Type: Conference Paper

References (26)

1. Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
2. Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1631–1643 (2005)
3. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR 2006, vol. 1 (2006)
4. Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: ICCV 2011, pp. 263–270. IEEE (2011)
5. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
7. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)
8. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
9. Kavukcuoglu, K., Sermanet, P., Boureau, Y.L., Gregor, K., Mathieu, M., LeCun, Y.: Learning convolutional feature hierachies for visual recognition. In: NIPS 2010
10. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS 2012 (2012)
11. Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: CVPR 2012 (2012)
12. Fan, J., Xu, W., Wu, Y., Gong, Y.: Human tracking using convolutional neural networks. Trans. Neural Netw. 21, 1610–1623 (2010)
13. Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: NIPS 2013 (2013)
14. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. Transactions on Pattern Analysis and Machine Intelligence (2011)
15. Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298–310. Springer, Heidelberg (2014)
16. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE (2012)
17. Cireșan, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
18. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Intl J. Comput. Vis. 88, 303–338 (2010)
19. Viola, P., Platt, J., Zhang, C., et al.: Multiple instance boosting for object detection. In: NIPS, vol. 2, p. 5 (2005)
20. Xing, J., Gao, J., Li, B., Hu, W., Yan, S.: Robust object tracking with online multilifespan dictionary learning. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 665–672. IEEE (2013)
21. Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: bootstrapping binary classifiers by structural constraints. In: CVPR 2010, pp. 49–56. IEEE (2010)
22. Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: CVPR 2011, pp. 1177–1184. IEEE (2011)
23. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR 2012, pp. 1822–1829. IEEE (2012)
24. Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: CVPR 2012, pp. 1838–1845. IEEE (2012)
25. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Intl. J. Comput. Vis. 77, 125–141 (2008)
26. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR 2013 (2013)