Multi-camera video object recognition using active contours

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


In this paper, we propose to tackle with multiple video-object detection and recognition in a multi-camera environment using active contours. Indeed, with the growth of multi-camera systems, many computer vision frameworks have been developed, but none taking advantage of the well-established active contour method. Hence, active contours allow precise and automatic delineation of entire object’s boundaries in frames, leading to an accurate segmentation and tracking of video objects displayed into the multi-view system, while our late fusion approach allows robust recognition of the detected objects in the synchronized sequences. Our active-contour-based system has been successfully tested on video-surveillance standard datasets and shows excellent performance in terms of computational efficiency and robustness compared to state-of-art ones.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Bio-Inspired Systems and Signal Processing (MPBS-2015)
Number of pages6
ISBN (Electronic)9789897580697
Publication statusPublished - 2015
Externally publishedYes


  • Active contours
  • Multi-camera detection
  • Unsupervised segmentation
  • Video-object recognition
  • Semantic colors
  • Multi-feature vector flow
  • Information fusion
  • Video surveillance
  • Scene understanding


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