Tracking multiple video objects based on improved joint probabilistic data association
改进联合概率数据关联的视频多目标快速跟踪

For the data association of video objects having little distinguishable features in large-scale monitoring scenes, we present a method for tracking multiple video objects in real-time based on the joint probabilistic data association (JPDA), in which the motion features of the objects are incorporated. First, the k-best joint events are computed by the Murty algorithm to reduce the complexity, and then, the motion situations of objects are analyzed by the association probability of JPDA. When objects are entering and exiting the field of view, merging and splitting (objects are detected as fragmented parts), the data association algorithm acquires the tracking trajectories of the objects. Experiments demonstrate the feasibility and performances of the proposed approach.