Vehicle detection and tracking in relatively crowded conditions

Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement - SEAP (Simple but Efficient After Process) which checks the detection results in an accurate way and is an after process of Adaboost detector which used to detect car in every frame. Second, we propose a tracking algorithm named 4-states tracking algorithm based on Kalman linear filter. Tracking results turn unsteady as traffic density grows higher because of much more false positives and false negatives appear. However, 4-states tracking algorithm can solve this problem in an easy way by introducing FSM (Finite State Machine) into tracking algorithm. Finally, we implement a real-time vehicle detection and tracking system with the upper methods. Experiments give good results in relative crowded Conditions.