• DocumentCode
    3266930
  • Title

    Pedestrian detection using hybrid statistical feature

  • Author

    Wu, Qiang ; Du, Chunhua ; Yang, Jie ; He, Xiangjian ; Chen, Yan

  • Author_Institution
    Sch. of Comput. & Commun., Univ. of Technol., Sydney, NSW
  • fYear
    2008
  • fDate
    8-10 Oct. 2008
  • Firstpage
    101
  • Lastpage
    106
  • Abstract
    A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA gait database and the additional non-human objects data.
  • Keywords
    feature extraction; image sequences; object detection; statistical analysis; CASIA gait database; gait energy image; human detection methods; hybrid statistical feature; nonhuman objects data; pedestrian detection; walking people detection; Assembly; Australia; Detectors; Humans; Layout; Legged locomotion; Motion detection; Spatial databases; Support vector machines; Surveillance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia Signal Processing, 2008 IEEE 10th Workshop on
  • Conference_Location
    Cairns, Qld
  • Print_ISBN
    978-1-4244-2294-4
  • Electronic_ISBN
    978-1-4244-2295-1
  • Type

    conf

  • DOI
    10.1109/MMSP.2008.4665056
  • Filename
    4665056