People detection in crowded scenes by context-driven label propagation

Jingjing Liu, Quanfu Fan, Sharath Pankanti, Dimitris N. Metaxas

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

1 Scopus citations

Abstract

Exploiting contextual cues has been a key idea to improve people detection in crowded scenes. Along this line we present a novel context-driven approach to detect people in crowded scenes. Based on a context graph that incorporates both geometric and social contextual patterns in crowds, we apply label propagation to discover weak detections contextually compatible with true detections while suppressing irrelevant false alarms. Compared to previous approaches for context modeling limited to only pairwise spatial interactions between local object neighbors, our approach provides a more effective way to model people interactions in a global context. Our approach achieves performance comparable to state of the art on two challenging datasets for people and pedestrian detection.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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