Multiple human detection and tracking based on weighted temporal texture features

In this paper, we present a method of tracking and identifying persons in video images taken by a fixed camera situated at an entrance. In video sequences a person may be totally or partially occluded in a scene for some period of time. The proposed approach uses the appearance model for the identification of persons and the weighted temporal texture features. The weight is related to the size, duration as well as the number of persons adjacent to the target person. Most systems have built an appearance model for each person to solve occlusion problems. The appearance model contains certain information on the target person. We have compared the proposed method with other related methods using color and shape features, and analyzed the features' stability. Experimental results with various real video data sequences revealed that real time person tracking and recognition is possible with increased stability in video surveillance applications even under situations of occasional occlusion.