Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210900070-6.doi: 10.11896/jsjkx.210900070

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Pedestrian Detection Optimization Method Based on Data Enhancement and SupervisedEqualization in Fisheye Image

SI Shao-feng1,2, ZHANG Sai-qiang1,2, LI Qing2, CHEN Ben-yao3   

  1. 1 University of Chinese Academy of Sciences,Beijing 100049,China
    2 Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,China
    3 Huzhou Special Equipment Inspection Center,Huzhou,Zhejiang 313000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:SI Shao-feng,born in 1996,postgra-duate.His main research interests include computer vision and object tra-cking.
    LI Qing,born in 1972,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence and multi-source information fusion.
  • Supported by:
    Research on Integrated Management and Application Technology of Elevator Safety(NQI20200125).

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Abstract: In recent years,due to the fisheye camera is widely used in the field of intelligent monitoring,many scholars propose pedestrian detection algorithm for fisheye image.However,the fisheye scene is complex and distorted,which leads to the imba-lance of data set sample distribution and algorithm supervision allocation,which will reduce the performance of the detector.To solve these problems,a data enhancement method for pedestrian detection in fisheye image is proposed,which consists of pattern sampling enhancement and angle histogram enhancement.The pattern sampling enhancement focuses on the mining of difficult samples in fisheye image,and the generated new samples enrich the pedestrian patterns near the center of fisheye image.Angle histogram enhancement is based on the idea of histogram equalization,which smooths the angle distribution of fisheye image samples to alleviate the over fitting problem of single scene.In addition,based on Anchor-free fisheye image pedestrian detector,the fusion of location quality prediction and supervision information is proposed to extend Focal Loss to continuous domain to optimize the supervision allocation of detector location branches.Experimental results show that the proposed data enhancement algorithm can effectively alleviate the uneven distribution of fisheye image data set,and show good results in both Anchor-Based and Anchor-Free detectors.The continuous Focal Loss optimization localization supervision method improves the overall performance by 3.8% without increasing the reasoning complexity of the Anchor-Free detector.

Key words: Fisheye images, Pedestrian detection, Data enhancement, Anchor-Free detector, Continuous Focal Loss

CLC Number: 

  • TP391
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