Detect to Track and Track to Detect

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 3057-3065

  Feichtenhofer, Christoph ^a   Pinz, Axel ^a   Zisserman, Andrew ^b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OBJECT RECOGNITION;

CORRELATION FEATURES; DETECTION AND TRACKING; HIGH-ACCURACY; LEVEL DETECTIONS; OBJECT CATEGORIES; SIMULTANEOUS DETECTION; SPATIO-TEMPORAL OBJECTS; STATE OF THE ART;

OBJECT DETECTION;

EID: 85041910397     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.330     Document Type: Conference Paper

References (40)

1. L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr. Fully-convolutional siamese networks for object tracking. In ECCV VOT Workshop, 2016.
2. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. Visual object tracking using adaptive correlation filters. In Proc. CVPR, 2010.
3. J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object detection via region-based fully convolutional networks. In NIPS, 2016.
4. M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg. Beyond correlation filters: Learning continuous convolution operators for visual tracking. In Proc. ECCV, 2016.
5. A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In Proc. ICCV, 2015.
6. C. Feichtenhofer, A. Pinz, and R. Wildes. Spatiotemporal residual networks for video action recognition. In NIPS, 2016.
7. C. Feichtenhofer, A. Pinz, and A. Zisserman. Convolutional two-stream network fusion for video action recognition. In Proc. CVPR, 2016.
8. S. Gidaris and N. Komodakis. Object detection via a multiregion and semantic segmentation-aware cnn model. In Proc. CVPR, 2015.
9. R. B. Girshick. Fast R-CNN. In Proc. ICCV, 2015.
10. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.
11. G. Gkioxari and J. Malik. Finding action tubes. In Proc. CVPR, 2015.
12. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proc. CVPR, 2016.
13. D. Held, S. Thrun, and S. Savarese. Learning to track at 100 FPS with deep regression networks. In Proc. ECCV, 2016.
14. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Highspeed tracking with kernelized correlation filters. IEEE PAMI, 37 (3):583-596, 2015.
15. A. Joulin, K. Tang, and L. Fei-Fei. Efficient image and video co-localization with frank-wolfe algorithm. In Proc. ECCV, 2014.
16. K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang. Object detection in videos with tubelet proposal networks. In Proc. CVPR, 2017.
17. K. Kang, H. Li, J. Yan, X. Zeng, B. Yang, T. Xiao, C. Zhang, Z. Wang, R. Wang, X. Wang, andW. Ouyang. T-CNN: tubelets with convolutional neural networks for object detection from videos. arXiv preprint, 2016.
18. K. Kang, W. Ouyang, H. Li, and X. Wang. Object detection from video tubelets with convolutional neural networks. In Proc. CVPR, 2016.
19. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106-1114, 2012.
20. S. Kwak, M. Cho, I. Laptev, J. Ponce, and C. Schmid. Unsupervised object discovery and tracking in video collections. In Proc. CVPR, 2015.
21. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1 (4):541-551, 1989.
22. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In Proc. ECCV, 2014.
23. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. Ssd: Single shot multibox detector. In Proc. ECCV, 2016.
24. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proc. CVPR, 2015.
25. C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang. Hierarchical convolutional features for visual tracking. In Proc. ICCV, 2015.
26. H. Nam and B. Han. Learning multi-domain convolutional neural networks for visual tracking. In Proc. CVPR, 2016.
27. X. Peng and C. Schmid. Multi-region two-stream R-CNN for action detection. In Proc. ECCV, 2016.
28. A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari. Learning object class detectors from weakly annotated video. In Proc. CVPR, 2012.
29. E. Real, J. Shlens, S. Mazzocchi, X. Pan, and V. Vanhoucke. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video. ArXiv e-prints, 2017.
30. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In Proc. CVPR, 2016.
31. S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE PAMI, 2016.
32. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. IJCV, 115 (3):211-252, 2015.
33. S. Saha, G. Singh, M. Sapienza, P. H. Torr, and F. Cuzzolin. Deep learning for detecting multiple space-time action tubes in videos. In Proc. BMVC., 2016.
34. A. Shrivastava, A. Gupta, and R. Girshick. Training regionbased object detectors with online hard example mining. In Proc. CVPR, 2016.
35. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In NIPS, 2014.
36. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proc. ICLR, 2015.
37. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proc. CVPR, 2015.
38. L. Wang, W. Ouyang, X. Wang, and H. Lu. Visual tracking with fully convolutional networks. In Proc. ICCV, 2015.
39. J. Yang, H. Shuai, Z. Yu, R. Fan, Q. Ma, Q. Liu, and J. Deng. ILSVRC2016 object detection from video: Team NUIST. http://image-net. org/challenges/talks/ 2016/Imagenet%202016%20VID. pptx, 2016.
40. X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei. Deep feature flow for video recognition. In Proc. CVPR, 2017.