Object Tracking and Primitive Event Detection by Spatio-Temporal Tracklet Association

Accurate object tracking is a challenging problem in visual surveillance due to noise segmentation, partial and full object occlusions. In this paper, we present a method for object tracking and primitive event detection by associating tracklet caused by these problems. The aim is to keep track identity across tracking gaps and detect object's motion changes (identify primitive event) that cause tracklet gaps. We first detect moving objects and generate tracklet, then grow these tracklets by finding the best spatial and temporal association of observations to track object across tracklet gaps and indentify the video event they involved. We successfully track multiple moving vehicles and persons under occlusion, noisy detections and split-merge situations and can identify the event that cause tracking gaps.