A closer look at Faster R-CNN for vehicle detection

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn 2016-August, Issue , 2016, Pages 124-129

  Fan, Quanfu ^a   Brown, Lisa ^a   Smith, John ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTELLIGENT VEHICLE HIGHWAY SYSTEMS; OBJECT DETECTION;

COMPREHENSIVE ANALYSIS; LARGE VEHICLES; PARAMETER-TUNING; STATE-OF-THE-ART PERFORMANCE; VEHICLE DETECTION;

VEHICLES;

EID: 84983246764     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2016.7535375     Document Type: Conference Paper

References (26)

1. Kitti benchmark, http://www. cvlibs. net/datasets/kitti/eval object. php.
2. L. M. Brown, Q. Fan, and Y. Zhai. Self-calibration from vehicle information. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on, pages 1-6. IEEE, 2015.
3. X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, and R. Urtasun. 3d object proposals for accurate object class detection. In NIPS, 2015.
4. X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun. 3d object proposals for accurate object class detection. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 424-432. Curran Associates, Inc., 2015.
5. P. Dollár, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(8):1532-1545, 2014.
6. R. Feris, R. Bobbitt, S. Pankanti, and M.-T. Sun. Efficient 24/7 object detection in surveillance videos. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on, pages 1-6. IEEE, 2015.
7. R. B. Girshick. Fast R-CNN. CoRR, abs/1504. 08083, 2015.
8. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In Computer Vision-ECCV 2014, pages 346-361. Springer, 2014.
9. M. Hejrati and D. Ramanan. Analyzing 3d objects in cluttered images. In Advances in Neural Information Processing Systems, pages 593-601, 2012.
10. D. Held, J. Levinson, and S. Thrun. A probabilistic framework for car detection in images using context and scale. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 1628-1634. IEEE, 2012.
11. L. Huang, Y. Yang, Y. Deng, and Y. Yu. Densebox: Unifying landmark localization with end to end object detection. CoRR, abs/1509. 04874, 2015.
12. B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, R. Cheng-Yue, F. Mujica, A. Coates, et al. An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504. 01716, 2015.
13. C. H. Lampert, M. B. Blaschko, and T. Hofmann. Efficient subwindow search: A branch and bound framework for object localization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(12):2129-2142, 2009.
14. B. Li, T. Wu, and S.-C. Zhu. Integrating context and occlusion for car detection by hierarchical and-or model. In ECCV, 2014.
15. C. Long, X. Wang, G. Hua, M. Yang, and Y. Lin. Accurate object detection with location relaxation and regionlets relocalization. In Asian Conference on Computer Vision, 2014.
16. H. T. Niknejad, A. Takeuchi, S. Mita, and D. McAllester. Onroad multivehicle tracking using deformable object model and particle filter with improved likelihood estimation. Intelligent Transportation Systems, IEEE Transactions on, 13(2):748-758, 2012.
17. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards realtime object detection with region proposal networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 91-99. Curran Associates, Inc., 2015.
18. S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506. 01497, 2015.
19. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409. 1556, 2014.
20. S. Sivaraman and M. M. Trivedi. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. Intelligent Transportation Systems, IEEE Transactions on, 14(4):1773-1795, 2013.
21. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. International journal of computer vision, 104(2):154-171, 2013.
22. X. Wang, M. Yang, S. Zhu, and Y. Lin. Regionlets for generic object detection. In T-PAMI, 2015.
23. Y. Xiang, W. Choi, Y. Lin, and S. Savarese. Data-driven 3d voxel patterns for object category recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2015.
24. J. J. Yebes, L. M. Bergasa, R. Arroyo, and A. Lazaro. Supervised learning and evaluation of kitti's cars detector with dpm. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages 768-773. IEEE, 2014.
25. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In Computer Vision-ECCV 2014, pages 391-405. Springer, 2014.
26. W. Y. Zou, X. Wang, M. Sun, and Y. Lin. Generic object detection with dense neural patterns and regionlets. In British Machine Vision Conference, 2014.