Authors:
Thierry Chesnais
1
;
Nicolas Allezard
1
;
Yoann Dhome
1
and
Thierry Chateau
2
Affiliations:
1
CEA, France
;
2
Blaise Pascal University, France
Keyword(s):
Video Surveillance, Object Detection, Pedestrian Detection, Semi-supervised Learning, Oracle.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
This article tackles the real-time pedestrian detection problem using a stationary uncalibrated camera. More precisely we try to specialize a classifier by taking into account the context of the scene. To achieve this goal, we introduce an offline semi-supervised approach which uses an oracle. This latter must automatically label a video, in order to obtain contextualized training data. The proposed oracle is composed of several detectors. Each of them is trained on a different signal: appearance, background subtraction and optical flow signals. Then we merge their responses and keep the more confident detections. A specialized detector is then built on the resulting dataset. Designed for improving camera network installation procedure, the presented method is completely automatic and does not need any knowledge about the scene.