Vehicle tracking based on shape information and inter-frame motion vector

Abstract

Vehicle tracking is one of the most fundamental aspects in traffic surveillance systems. However, there is always a trade–off between tracking accuracy and speed. Tracking algorithms with excellent tracking accuracy tend to have low processing speeds, whereas fast tracking algorithms usually have low tracking accuracy. This paper proposes a vehicle tracking algorithm based on the simple motion of vehicles in traffic surveillance videos. In the proposed algorithm, an inter–frame motion vector is defined using vehicle speed and acceleration in the current frame to forecast the vehicle speed in subsequent frames. Further, speed and shape error functions are defined to match the target. The results of experiments conducted comparing the proposed method with kernelized correlation filter, multiple instance learning, and Kalman filter indicate that only the proposed algorithm achieves a balance in terms of both speed and accuracy, resulting in satisfactory tracking accuracy and meeting real–time requirements.