Counting Vehicles in Highway Surveillance Videos

Abstract

This paper presents a complete system for accurately and efficiently counting vehicles in a highway surveillance video. The proposed approach employs vehicle detection and tracking modules. In the detection module, an automatically trained binary classifier detects vehicles while providing robustness against view-point, poor quality videos and clutter. Efficient tracking is then achieved by a simplified multi-hypothesis approach. First an over-complete set of tracks is created considering every observed detection within a time interval. As needed, hypothesized detections are generated to force continuous tracks. Finally, a scoring function is used to separate the valid tracks in the over-complete set. Our tracking system achieved accurate results in significantly challenging highway surveillance videos.