Object detection from video tubelets with convolutional neural networks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 817-825

  Kang, Kai ^a   Ouyang, Wanli ^a   Li, Hongsheng ^a   Wang, Xiaogang ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; IMAGE PROCESSING; IMAGE SEGMENTATION; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN RECOGNITION; SEMANTICS;

BOUNDING BOX; CONVOLUTION NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORK; DETECTION FRAMEWORK; OBJECT TRACKING; SEMANTIC SEGMENTATION; STILL IMAGES; TEMPORAL INFORMATION;

OBJECT DETECTION;

EID: 84986331475     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.95     Document Type: Conference Paper

References (38)

1. M. Andriluka, S. Roth, and B. Schiele. People-tracking-bydetection and people-detection-by-tracking. CVPR, 2008.
2. A. Andriyenko, K. Schindler, and S. Roth. Discretecontinuous optimization for multi-target tracking. CVPR, 2012.
3. S.-H. Bae and K.-J. Yoon. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. CVPR, 2014.
4. X. Chu, W. Ouyang, H. Li, and X. Wang. Structured feature learning for pose estimation. In CVPR, 2016.
5. J. Dai, K. He, and J. Sun. Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. ICCV, 2015.
6. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. CVPR, 2009.
7. T. Deselaers, B. Alexe, and V. Ferrari. Localizing Objects While Learning Their Appearance. ECCV, 2010.
8. H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt, et al. From captions to visual concepts and back. CVPR, 2014.
9. R. Girshick. Fast r-cnn. ICCV, 2015.
10. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 2014.
11. G. Gkioxari and J. Malik. Finding action tubes. CVPR, 2014.
12. Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao. Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking. CVPR, 2015.
13. M. Jain, J. Van Gemert, H. Jégou, P. Bouthemy, and C. G. Snoek. Action localization with tubelets from motion. CVPR, 2014.
14. A. Joulin, K. Tang, and L. Fei-Fei. Efficient Image and Video Co-localization with Frank-Wolfe Algorithm. ECCV, 2014.
15. K. Kang, H. Li, J. Yan, X. Zeng, B. Yang, T. Xiao, C. Zhang, Z. Wang, R. Wang, X. Wang, and W. Ouyang. T-cnn: Tubelets with convolutional neural networks for object detection from videos. ArXiv preprint arXiv: 1604. 02532, 2016.
16. K. Kang and X. Wang. Fully convolutional neural networks for crowd segmentation. ArXiv preprint arXiv: 1411. 4464, 2014.
17. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. NIPS, pages 1097-1105, 2012.
18. S. Kwak, M. Cho, I. Laptev, J. Ponce, and C. Schmid. Unsupervised Object Discovery and Tracking in Video Collections. ICCV, 2015.
19. B. Li, T. Wu, and S.-C. Zhu. Integrating context and occlusion for car detection by hierarchical and-or model. ECCV, 2014.
20. Y. Li, J. Zhu, and S. C. Hoi. Reliable patch trackers: Robust visual tracking by exploiting reliable patches. CVPR, 2015.
21. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
22. H. Nam and B. Han. Learning multi-domain convolutional neural networks for visual tracking. ArXiv: 1510. 07945, 2015.
23. W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, et al. DeepID-net: Deformable deep convolutional neural networks for object detection. CVPR, 2015.
24. W. Ouyang, X. Wang, C. Zhang, and X. Yang. Factors in finetuning deep model for object detection with long-tail. In CVPR, 2016.
25. A. Papazoglou and V. Ferrari. Fast Object Segmentation in Unconstrained Video. ICCV, 2013.
26. H. Possegger, T. Mauthner, P. M. Roth, and H. Bischof. Occlusion geodesics for online multi-object tracking. CVPR, 2014.
27. A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari. Learning object class detectors from weakly annotated video. CVPR, 2012.
28. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. NIPS, 2015.
29. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2014.
30. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CVPR, 2015.
31. Y. Tian, P. Luo, X. Wang, and X. Tang. Pedestrian detection aided by deep learning semantic tasks. CVPR, 2014.
32. J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler. Efficient object localization using convolutional networks. In CVPR, 2015.
33. A. Toshev and C. Szegedy. Deeppose: Human pose estimation via deep neural networks. In CVPR, 2014.
34. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. IJCV, 104 (2): 154-171, 2013.
35. L. Wang, W. Ouyang, X. Wang, and L. Huchuan. Stct: Sequentially training convolutional networks for visual tracking. In CVPR, 2016.
36. L. Wang, W. Ouyang, X. Wang, and H. Lu. Visual tracking with fully convolutional networks. ICCV, 2015.
37. W. Yang, W. Ouyang, H. Li, and X. Wang. End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation. In CVPR, 2016.
38. G. Yu and J. Yuan. Fast action proposals for human action detection and search. CVPR, 2015.