Multi-view and multi-plane data fusion for effective pedestrian detection in intelligent visual surveillance

Jie Ren*, Ming Xu, Jeremy S. Smith, Shi Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


For the robust detection of pedestrians in intelligent video surveillance, an approach to multi-view and multi-plane data fusion is proposed. Through the estimated homography, foreground regions are projected from multiple camera views to a reference view. To identify false-positive detections caused by foreground intersections of non-corresponding objects, the homographic transformations for a set of parallel planes, which are from the head plane to the ground, are applied. Multiple features including occupancy information and colour cues are extracted from such planes for joint decision-making. Experimental results on real world sequences have demonstrated the good performance of the proposed approach in pedestrian detection for intelligent visual surveillance.

Original languageEnglish
Pages (from-to)1007-1029
Number of pages23
JournalMultidimensional Systems and Signal Processing
Issue number4
Publication statusPublished - 1 Oct 2016


  • Detection
  • Homography
  • Information fusion
  • Video surveillance

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