Authors:
Adolfo Lopez-Mendez
;
Florent Monay
and
Jean-Marc Odobez
Affiliation:
IDIAP Research Institute, Switzerland
Keyword(s):
Dropped Object Detection, Human Detection, Background Subtraction, Geometry Constraints.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
This paper presents a method for the automated detection of dropped objects in surveillance scenarios, which is a very important task for abandoned object detection. Our method works in single views and exploits prior information of the scene, such as geometry or the fact that a number of false alarms are caused by known objects, such as humans. The proposed approach builds dropped object candidates by analyzing blobs obtained with a multi-layer background subtraction approach. The created dropped object candidates are
then characterized both by appearance and by temporal aspects such as the estimated drop time. Next, we incorporate prior knowledge about the possible sizes and positions of dropped objects through an efficient filtering approach. Finally, the output of a human detector is exploited over in order to filter out static objects that are likely to be humans that remain still. Experimental results on the publicly available PETS2006 datasets and on several long sequences rec
orded in metro stations show the effectiveness of the proposed approach.
Furthermore, our approach can operate in real-time.
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