%0 Conference Proceedings
%T CSSD: An End-to-End Deep Neural Network Approach to Pedestrian Detection
%+ National University of Defense Technology [China]
%A Wei, Feifan
%A Xie, Jianbin
%A Yan, Wei
%A Li, Peiqin
%Z Part 5: Perceptual Intelligence
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 2nd International Conference on Intelligence Science (ICIS)
%C Beijing, China
%Y Zhongzhi Shi
%Y Cyriel Pennartz
%Y Tiejun Huang
%I Springer International Publishing
%3 Intelligence Science II
%V AICT-539
%P 245-254
%8 2018-11-02
%D 2018
%R 10.1007/978-3-030-01313-4_26
%K Deep learning
%K Pedestrian detection
%K One-stage detector
%Z Computer Science [cs]Conference papers
%X Single Shot Multibox Detector (SSD) provides a powerful framework for detecting objects using a single deep neural network. The detection framework is one of the top object detection algorithms in both accuracy and speed which processes a large set of object locations sampled across an image. However, this framework does not behave well for the task of pedestrian detection since the images in popular pedestrian datasets have multiple objects occlusion problem and contain lots of small objects. In this paper, we incorporate deconvolution and downsampling unit into the SSD framework allowing detection network to recycle feature maps learned from images. The enhanced performance was obtained by changing the structure of classifier network, e.g., by replacing VGGNet with DenseNet. The contribution of this paper is a one-stage approach to compose a single deep neural network for pedestrian detection task in real-time. This approach addresses the typical difficulty of detecting different scale pedestrian at only one layer by providing a novel channel fusion. To solve small objects problem, base network has been replaced with more powerful one. This approach outperforms competing one-single methods on standard Caltech pedestrian dataset benchmark. It is also faster than all the other methods.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-02118815/document
%2 https://inria.hal.science/hal-02118815/file/474230_1_En_26_Chapter.pdf
%L hal-02118815
%U https://inria.hal.science/hal-02118815
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICIS
%~ IFIP-AICT-539