Hybrid Camera-Radar Vehicle Tracking with Image Perceptual Hash Encoding

For sensing system, the trustworthiness of the variant sensors is the crucial point when dealing with advanced driving assistant system application. In this paper, an approach to a hybrid camera-radar application of vehicle tracking is presented, able to meet the requirement of such demand. Most of the time, different types of commercial sensors available nowadays specialize in different situations, such as the ability of offering a wealth of detailed information about the scene for the camera or the powerful resistance to the severe weather for the millimeter-wave (MMW) radar. The detection and tracking in different sensors are usually independent. Thus, the work here that combines the variant information provided by different sensors is indispensable and worthwhile. For the real-time requirement of merging the measurement of automotive MMW radar in high speed, this paper first proposes a fast vehicle tracking algorithm based on image perceptual hash encoding. Then, for sake of efficiency, the tracked target scale is estimated by utilizing the target range measurement from the MMW radar. A series of publicly challenging benchmark image sequences have been tested. The tracking results indicate that the proposed tracker is feasible and effective. Plus, the actual experiments about vehicle tracking are taken to further testify the tracker’s performance. The results show that the proposed tracker can track the vehicle well and update the scale correctly.