Abstract
Pedestrian detection is a special topic in computer vision and plays a key role in intelligent vehicles and unmanned drive. Although recent pedestrian detect methods such as RPN_BF [1] have shown good performance from visible spectrum images at daytime, they have limited study for near-infrared image at nighttime. Unfortunately, when the traffic accident happened at night, the pedestrian is one of the most serious victims. Recently deep convolutional neural networks such as R-CNN/Faster R-CNN [2, 3] have shown excellent performance for object detection. In this paper, we investigate issues involving Faster R-CNN for construction of end-to-end all-day pedestrian detection system. We propose an effective baseline for pedestrian detection both on visible spectrum images and infrared images, using a same pre-train Faster R-CNN model. We comprehensively evaluate this method, the experiment results presenting competitive accuracy and acceptable running time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: ECCV 2016, 1–15 (2016)
Girshick, R., Donahue, J., Darrell, T., Berkeley, U.C., Malik, J.: R-CNN: region-based convolutional neural networks. In: CVPR 2014, 2–9 (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, 1–10 (2015)
World Health Organization: Global Status Report on Road Safety 2015 (2015)
Yan, J., Zhang, X., Lei, Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: IEEE Conference Computer Vision Pattern Recognition, 3033–3040 (2013)
Dollár, P., Wojek, C.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)
Zhang, X., Chen, G., Saruta, K., Terata, Y.: Discriminative feature points distribution in near-infrared pedestrian images. IEEJ Trans. Electron. Inf. Syst. 135, 1222–1228 (2015)
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. 104, 154–171 (2013)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)
Zhang, S., Bauckhage, C., Cremers, A.B.: Informed Haar-like features improve pedestrian detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 947–954 (2014)
Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1532–1545 (2014)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC 2009, London, England, 1–11 (2009)
Ouyang, W., Wang, X.: A discriminative deep model for pedestrian detection with occlusion handling. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3258–3265 (2012)
Ouyang, W., Zeng, X., Wang, X.: Modeling mutual visibility relationship in pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3222–3229 (2013)
Luo, P., Tian, Y., Wang, X., Tang, X.: Switchable deep network for pedestrian detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 899–905 (2014)
Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks. In: Proceedings of IEEE Computer Society Conference Computer Vision Pattern Recognition, 7–12 June 2015, 5079–5087 (2015)
Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., Tubaro, S.: Deep convolutional neural networks for pedestrian detection. Arxiv (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)
Girshick, R.: Fast R-CNN: fast region-based convolutional networks for object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 1440–1448 (2016)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR, 1–14 (2014)
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. 115, 211–252 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, X., Chen, G., Saruta, K., Terata, Y. (2017). Deep Convolutional Neural Networks for All-Day Pedestrian Detection. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_21
Download citation
DOI: https://doi.org/10.1007/978-981-10-4154-9_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4153-2
Online ISBN: 978-981-10-4154-9
eBook Packages: EngineeringEngineering (R0)