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Object detection and tracking are two fundamental tasks in video camera surveillance. In the case of moving object detection and tracking, an integrated Kalman Filter based system can be used. Automatic object detection is usually the first task in a camera-based surveillance system and background modelling (BM) is commonly used to extract predefined information such as object’s shape, geometry and etc., for further processing. But occlusion handling is required to perform advanced recognition and segmentation processes. Training based could be employed for recognition purposes on the frame outputs of a single surveillance camera video. A HoG feature based SVM trained classifier can be utilized for the recognition. The proposed framework configuration is not quite the same as existing multi-camera observation frameworks which use basic image data extricated from comparable field of vision (FOVs) to enhance the object discovery and tracking execution.

Multiclass SVM and HoG based object recognition of AGMM detected and KF tracked moving objects from single camera input video