Authors:
Marco Cristani
and
Vittorio Murino
Affiliation:
Università degli Studi of Verona, Italy
Keyword(s):
Surveillance, Motion analysis, Robustness.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
Abstract:
In the literature, visual surveillance methods based on joint pixel and region analysis for background subtraction are proven to be effective in discovering foreground objects in cluttered scenes. Typically, per-pixel foreground detection is contextualized in a local neighborhood region in order to limit false alarms. However, such methods have an heavy computational cost, depending on the size of the surrounding region considered for each pixel. In this paper, we propose an original and efficient joint pixel-region analysis technique able to automatically select the sampling rate with which pixels in different areas are checked out, while adapting the size of the neighborhood region considered. The algorithm has been validated on standard videos with benchmark tests, proving the goodness of the approach, especially in terms of quality of the detection with respect to the frame rate achieved.