Is faster R-CNN doing well for pedestrian detection?

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

Volumn 9906 LNCS, Issue , 2016, Pages 443-457

  Zhang, Liliang ^a   Lin, Liang ^a   Liang, Xiaodan ^a   He, Kaiming ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Boosted forests; Convolutional neural networks; Hard negative mining; Pedestrian detection

Indexed keywords

COMPUTER VISION; CONVOLUTION; FEATURE EXTRACTION; FORESTRY; NEURAL NETWORKS; OBJECT RECOGNITION;

BOOSTED FORESTS; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; HIGH RESOLUTION; HYBRID METHOD; NEGATIVE EXAMPLES; NEGATIVE MININGS; PEDESTRIAN DETECTION;

OBJECT DETECTION;

EID: 84990855988     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46475-6_28     Document Type: Conference Paper

References (32)

1. Hosang, J., Omran, M., Benenson, R., Schiele, B.: Taking a deeper look at pedestrians. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
2. Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
3. Tian, Y., Luo, P., Wang, X., Tang, X.: Deep learning strong parts for pedestrian detection. In: IEEE International Conference on Computer Vision (ICCV) (2015)
4. Cai, Z., Saberian, M., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: IEEE International Conference on Computer Vision (ICCV) (2015)
5. Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: British Machine Vision Conference (BMVC) (2009)
6. Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36, 1532-1545 (2014)
7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS) (2012)
8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv: 1409.1556
9. Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014Workshops. LNCS, vol. 8926, pp. 613-627. Springer, Heidelberg (2015)
10. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
11. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2015)
12. Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision (ICCV) (2015)
13. Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. (IJCV) 104, 154-171 (2013)
14. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34, 743-761 (2012)
15. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 346-361. Springer, Heidelberg (2014)
16. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets, fully connected CRFs (2014). arXiv: 1412.7062
17. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
18. Friedman, J., Hastie, T., Tibshirani, R., et al.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28, 337-407 (2000)
19. Appel, R., Fuchs, T., Dollár, P., Perona, P.: Quickly boosting decision trees-pruning underachieving features early. In: International Conference on Machine Learning (ICML) (2013)
20. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)
21. Ess, A., Leibe, B., Gool, L.V.: Depth and appearance for mobile scene analysis. In: IEEE International Conference on Computer Vision (ICCV) (2007)
22. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
23. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. (IJCV) 57, 137-154 (2004)
24. Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Neural Information Processing Systems (NIPS) (2014)
25. Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: IEEE International Conference on Computer Vision (ICCV) (2015)
26. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115, 211-252 (2015)
27. Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: Looking wider to see better (2015). arXiv: 1506.04579
28. Dollár, P.: Piotr’s Computer Vision Matlab Toolbox (PMT). https://github.com/ pdollar/toolbox
29. Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
30. Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Strengthening the effectiveness of pedestrian detection with spatially pooled features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 546-561. Springer, Heidelberg (2014)
31. Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
32. Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining (2016). arXiv: 1604.03540