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.
Also, Chesnais et al. REF proposed an oracle composed of three independent classifiers (appearance, background extraction, and optical flow).
Automatic Process to Build a Contextualized Detector
VISAPP
Computer Science