Deep Convolutional Neural Networks for pedestrian detection

Signal Processing: Image Communication

Volumn 47, Issue , 2016, Pages 482-489

  Tome D ^a   Monti, F ^a   Baroffio, L ^a   Bondi, L ^a   Tagliasacchi, M ^a   Tubaro, S ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Convolutional Neural Networks; Deep learning; Optimization; Pedestrian detection

Indexed keywords

COMPUTER VISION; CONVOLUTION; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; NEURAL NETWORKS; OBJECT RECOGNITION; OPTIMIZATION;

COMPUTATIONAL TIME; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; PEDESTRIAN DETECTION; PEDESTRIAN DETECTION SYSTEM; STATE OF THE ART; STATE-OF-THE-ART APPROACH;

OBJECT DETECTION;

EID: 84971631219     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2016.05.007     Document Type: Article

References (31)

1. [1] Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34:11 (2012), 2189–2202.
2. [2] R. Benenson, M. Omran, J. Hosang, B. Schiele, Ten years of pedestrian detection, what have we learned? in: Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD, ECCV workshop), Sept 2014.
3. [3] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1. pp. 886–893, June 2005.
4. [4] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database, in: CVPR, 2009.
5. [5] Dollar, P., Appel, R., Belongie, S., Perona, P., Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36:8 (2014), 1532–1545.
6. [6] P. Dollár, Z. Tu, P. Perona, S. Belongie, Integral channel features. In: Proceedings of the British Machine Vision Conference, BMVA Press, 2009, pp. 91.1–91.11. 〈 http://dx.doi.org/10.5244/C.23.91〉.
7. [7] Dollar, P., Wojek, C., Schiele, B., Perona, P., Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34:4 (2012), 743–761.
8. [8] I. Endres, D. Hoiem, Category independent object proposals, in: Computer Vision–ECCV, Springer, 2010, pp. 575–588.
9. [9] Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D., Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32:9 (2010), 1627–1645.
10. [10] Freund, Y., Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55:1 (1997), 119–139.
11. [11] Girshick, R., Donahue, J., Darrell, T., Malik, J., Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. PP Pattern Anal. Mach. Intell., 99, 2015 1–1.
12. [12] J. Hosang, M. Omran, R. Benenson, B. Schiele, Taking a deeper look at pedestrians, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
13. [13] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: convolutional architecture for fast feature embedding, in: Proceedings of the ACM International Conference on Multimedia, 2014, pp. 675–678.
14. [14] S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, H. Winnemoeller, Recognizing image style, arXiv preprint, 2013. 〈 arXiv:1311.3715〉.
15. [15] A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
16. [16] Lecun, Y., Bengio, Y., Convolutional Networks for Images, Speech and Time Series. 1995, The MIT Press, 255–258.
17. [17] W. Nam, P. Dollár, J.H. Han, Local decorrelation for improved pedestrian detection, in: Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014.
18. [18] Nguyen, D.T., Li, W., Ogunbona, P.O., Human detection from images and videos: a survey. Pattern Recognit. 51 (2016), 148–175.
19. [19] W. Ouyang, X. Wang, A discriminative deep model for pedestrian detection with occlusion handling, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3258–3265.
20. [20] W. Ouyang, X. Wang, Joint deep learning for pedestrian detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Dec 2013, pp. 2056–2063.
21. [21] S. Paisitkriangkrai, C. Shen, A.v.d. Hengel, Pedestrian detection with spatially pooled features and structured ensemble learning, arXiv preprint, 2014. 〈 arXiv:1409.5209〉.
22. [22] R. Polana, R. Nelson, Detecting activities, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Jun 1993, pp. 2–7.
23. [23] P. Sermanet, K. Kavukcuoglu, S. Chintala, Y. LeCun, Pedestrian detection with unsupervised multi-stage feature learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013, pp. 3626–3633.
24. [24] G. Stockman, L.G. Shapiro, Computer Vision, (1st ed.), Prentice Hall PTR, Upper Saddle River, NJ, USA, 2001.
25. [25] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: CVPR, 2015. 〈 arXiv:1409.4842〉.
26. [26] Y. Taigman, M. Yang, M. Ranzato, L. Wolf, . Deepface: Closing the gap to human-level performance in face verification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014, pp. 1701–1708.
27. [27] Y. Tian, P. Luo, X. Wang, X. Tang, Pedestrian detection aided by deep learning semantic tasks, arXiv preprint, 2014. 〈 arXiv:1412.0069〉.
28. [28] J. Uijlings, K. van de Sande, T. Gevers, A. Smeulders, Selective search for object recognition, International Journal of Computer Vision, 2013.
29. [29] P. Viola, M. Jones, D. Snow, Detecting pedestrians using patterns of motion and appearance, in: Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, Oct 2003, pp. 734–741.
30. [30] B. Yang, J. Yan, Z. Lei, S.Z. Li, Convolutional channel features, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 82–90.
31. [31] S. Zhang, R. Benenson, B. Schiele, Filtered channel features for pedestrian detection, arXiv preprint, 2015. 〈 arXiv:1501.05759〉.