Studies on pedestrians using microscopic simulation require large amounts of trajectory data from real-world pedestrian crowds. The collection of such data, if done manually, involves tremendous efforts and is very time-consuming. Although many studies have asserted the possibility of automating this task using video cameras, we have found that only a few have demonstrated good performance in very crowded situations or from a top-angled view scene. This paper deals with tracking pedestrian crowd under heavy occlusion and from an angular scene using only a single non-stereo video camera. Our automated tracking system consists of three modules that are performed sequentially. The first module detects moving objects as blobs. The second module computes feature values from the blob information in order to generate what we call a possibility matrix. The third module is a tracking system, which employs a Bayesian update of the probability tree derived from the possibility matrix and from the detection of each pedestrian, in order to track the next position of the pedestrian. The result of such tracking is a database of pedestrian trajectories over time and space. With certain prior information, we show that the system is able to track a large number of people under occlusion and clutter scene.

Studies on microscopic pedestrian requires large amounts of trajectory data from real-world pedestrian crowds. Such data collection, if done manually, needs tremendous effort and is very time consuming. Though many studies have asserted the possibility of automating this task using video cameras, we found that only a few have demonstrated good performance in very crowded situations or from a top-angled view scene. This paper deals with tracking pedestrian crowd under heavy occlusions from an angular scene. Our automated tracking system consists of two modules that perform sequentially. The first module detects moving objects as blobs. The second module is a tracking system. We employ probability distribution from the detection of each pedestrian and use Bayesian update to track the next position.The result of such tracking is a database of pedestrian trajectories over time and space. With certain prior information, we showed that the system can track a large number of people under occlusion and clutter scene.