Robust Pedestrian Detection Based on Parallel Channel Cascade Network
HE Jiaojiao1,2, ZHANG Yongping2, YAO Tuozhong2, LIU Ken2,3, XIAO Jiangjian4
1.School of Electronic Control, Chang'an University, Xi'an 710064 2.School of Electronic and Information Engineering, Ningbo University of Techology, Ningbo 315016 3.School of Information Engineering, Chang'an University, Xi'an 710064 4.Advanced Manufacturing Institute Computer Vision Team, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201
Abstract In the wide-angle field with perspective distortion, the resolution of distant pedestrian is low and there is distortion in a broad range of scales. Aiming at these problems, a robust pedestrian detection algorithm based on parallel channel cascade network is proposed. Firstly, differential information is introduced as weak supervisory information. Secondly, a new feature extraction network, channel cascade network(CCN), is proposed. On this basis, a parallel CCN is designed, and the difference map and the original map are utilized as its input. More abundant image features are fused. Finally, in the region proposal network, the distribution of pedestrians in the picture is characterized by clustering, and anchors meeting the pedestrian's characteristics are clustered. Experimental results show that the proposed algorithm is better than the standard Faster-RCNN algorithm and FPN algorithm for small-scale pedestrian detection in the presence of wide-angle field of view distortion.
Fund:Supported by National Natural Science Fundation(No.61771270), Zhejiang Natural Science Fundation Project(No.2017A610109,LQ15F020004), Zhejiang Key Research and Development Program(No.2018C01086), National Key Technology R&D Program(No.2015BAF14B01), Ningbo Natural Science Fundation Project(No.2018A610160)
About author:: (HE Jiaojiao, master student. His research interests include computer vision and image processing.) (ZHANG Yongping(Corresponding author), Ph.D., professor. His research inte-rests include computer vision, pattern recognition, image processing and analysis.) (YAO Tuozhong, Ph.D., lecturer. His search interests include omni directional vision sensor, wide area multi-target tracking algorithm, finite element method and its application.) (LIU Ken, master student. His research interests include computer vision and image processing.) (XIAO Jiangjian, Ph.D., researcher. His research interests include computer vision, video processing, vehicle tracking and intelligent transportation, pattern recognition.)
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2018, Vol. 31 
