Pedestrian detection in crowded scenes via scale and occlusion analysis

Lu Wang, Lisheng Xu, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate initial detections. Scale prior at each image location is estimated using the cues provided by neighboring detections, and the confidence score of each detection is refined according to its consistency with the estimated scale prior. Local occlusion analysis is exploited in refining detection confidence scores which facilitates the final detection cluster based Non-Maximum Suppression. Experimental results on benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages1210-1214
Number of pages5
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 2016 Aug 3
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 2016 Sep 252016 Sep 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period16/9/2516/9/28

Bibliographical note

Funding Information:
Lu Wang is supported in part by Chinese Scholarship Council, NSFC #61202258 and the Fundamental Research Funds for Central Universities of China #N130404016.

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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