Utilizing Attributed Graph Representation in Object Detection and Tracking for Indoor Range Sensor Surveillance Cameras

The problem of identifying moving objects in a video recording produced by a range sensor camera is due to the limited information available for classifying different objects. On the other hand, the infrared signal from a range sensor camera is more robust for extreme luminance intensity when the monitored area has light conditions that are too bright or too dark. This paper proposes a method of detection and tracking moving objects in image sequences captured by stationary range sensor cameras. Here, the depth information is utilized to correctly identify each of detected objects. Firstly, camera calibration and background subtraction are performed to separate the background from the moving objects. Next, a 2D projection mapping is performed to obtain the location and contour of the objects in the 2D plane. Based on this information, graph matching is performed based on features extracted from the 2D data, namely object position, size and the behavior of the objects. By observing the changes in the number of objects and the objects' position relative to each other, similarity matching is performed to track the objects in the temporal domain. Experimental results show that by using similarity matching, object identification can be correctly achieved even during occlusion.