Multi-view visual surveillance and phantom removal for effective pedestrian detection

To increase the robustness of detection in intelligent video surveillance systems, homography has been widely used to fuse foreground regions projected from multiple camera views to a reference view. However, the intersections of non-corresponding foreground regions can cause phantoms. This paper pr...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-07, Vol.77 (14), p.18801-18826
Main Authors: Ren, Jie, Xu, Ming, Smith, Jeremy S., Zhao, Huimin, Zhang, Rui
Format: Article
Language:English
Subjects:
Algorithms
Color
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Intersections
Multimedia Information Systems
Robustness
Special Purpose and Application-Based Systems
Surveillance
Surveillance systems
Video data
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Summary:To increase the robustness of detection in intelligent video surveillance systems, homography has been widely used to fuse foreground regions projected from multiple camera views to a reference view. However, the intersections of non-corresponding foreground regions can cause phantoms. This paper proposes an algorithm based on geometry and colour cues to cope with this problem, in which the homography between different camera views and the Mahalanobis distance between the colour distributions of every two associated foreground regions are considered. The integration of these two matching algorithms improves the robustness of the pedestrian and phantom classification. Experiments on real-world video sequences have shown the robustness of this algorithm.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4939-8