Cui Hua

Multi-feature Detection for Pedestrian Tracking in Traffic Surveillance

Pedestrian tracking is an active research area to improve traffic safety for intelligent video surveillance. This paper proposes an efficient method to automatically detect and track far-away pedestrians in surveillance video using the motion feature extraction and analysis. Firstly, pedestrian features of each frame are extracted by object segmentation, recognition and feature extraction. Then, the similar features in current frame image of all candidate objects are matched by the characteristic information of pedestrians in the previous frame which is considered as a template. Finally, pedestrian trajectory analysis algorithms are used on the track trajectories and the motion information can be attained, which can realize the early classification warning of pedestrian events. Experimental results in practical surveillance demonstrate that this method shorten the processing time of matching pedestrians and improve the reliability and real-time ability of pedestrian tracking.

A Point-Based Tracking Algorithm for Vehicle Trajectories in Complex Environment

In this paper, a point-based tracking algorithm is presented, which can be used in traffic jams and complex weather conditions. The main approaches for tracking vehicle trajectories are based on accurately segment for the moving vehicles, while uneven illumination, shadows and vehicle overlapping are difficult to handle. The main contribution of this paper is to propose a point tracking algorithm for vehicle trajectories without a difficult image segmentation procedure. In the proposed algorithm, feature points are extracted using an improved Moravec algorithm. A specially designed template is used to track the feature points through the image sequences. Then trajectories of feature points can be obtained, while unqualified track trajectories are removed using decision rules. The experiment results show that the algorithm is robust enough for vehicle tracking in complex weather conditions.