Abstract
At present, there are several new challenges for multi-scale pedestrian detection in wide-angle field of view, especially small-size pedestrians. So the problem is how we can detect pedestrians efficiently and accurately with limited resources in wide-angle field of vision. In this work, we propose a Channel-Cascading pedestrian detection network for small-size pedestrians. In combination with the two-stage idea of Faster-RCNN in our detector, the optimized network was applied and the regional proposal network was improved. We propose a novel feature extraction network as optimized network, which we call the “Channel-Cascading Network” (CCN), that fuses information between channels by progressive cascading strategy and adapts our idea to other network designs. The experimental results show that our detector performs better for small-size pedestrians, it not only the precision of pedestrian detection in wide field of view is greatly improved especially small-size pedestrian, but also the speed is accelerated.
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
References
Zhang, Q.: Research on pedestrian detection methods on still images. University of Science and Technology of China (2015). (In Chinese)
Lin, T.Y., et al.: Feature pyramid networks for object detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 936–944 (2016)
Wang, B.: Pedestrian Detection Based on Deep Learning. Beijing Jiaotong University (2015). (In Chinese)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Zhu, Q., et al.: Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498 (2006)
Chen, P.H., Lin, C.J.: A Tutorial on -support vector machines. In: Applied Stochastic Models in Business & Industry, vol. 21, No. 2, pp. 111–136 (2005)
Felzenszwalb, P.F., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Wang, X.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of IEEE International Conference on Computer Vision, September, Kyoto, Japan, pp. 32–39 (2009)
Kuo, W., Hariharan, B., Malik, J.: DeepBox: learning objectness with convolutional networks. In: IEEE International Conference on Computer Vision, pp. 2479–2487 (2015)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Girshick, R.: Fast R-CNN. Computer Science, pp. 1440–1448 (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems, pp. 91–9 (2015)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2016)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 21–37 (2016)
He, K., et al.: Deep residual learning for image recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)
Kong, T., et al.: HyperNet: towards accurate region proposal generation and joint object detection. In: Computer Vision and Pattern Recognition (2016)
Cai, Z., Fan, Q., Feris, Rogerio, S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 354–370. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_22
Hariharan, B., et al.: Hypercolumns for object segmentation and fine-grained localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 447–456 (2014)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Sermanet, P., Kavukcuoglu, K., Chintala, S., et al.: Pedestrian detection with unsupervised multi-stage feature learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626–3633 (2013)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Computer Science. pp. 730–734 (2014)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (N0. 61771270), the Natural Science Foundation of Zhejiang Province (No. 2017A610109) and (LQ15F020004), Key research and development plan of Zhejiang province (2018C01086).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
He, J. et al. (2018). A Channel-Cascading Pedestrian Detection Network for Small-Size Pedestrians. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-02698-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02697-4
Online ISBN: 978-3-030-02698-1
eBook Packages: Computer ScienceComputer Science (R0)