Illegally parked vehicle detection using deep learning and key-point tracking

摘要

In this paper, we present a method for identifying and tracking illegally parked vehicles. This approach is based on deep learning for vehicles detection and hand crafted descriptors for the tracking which are designed to cope with occlusions. The tracking of the parked vehicle is achieved by key-point extraction of the detected vehicles and feature point matching. For each frame, a bounding box was generated to represent the vehicle and feature points extracted in that area. All parked vehicles have a unique ID which was generated by the Hungarian algorithm and Kalman filter, and the parked vehicle with the same ID was matched frame by frame. Based on this matching result, the stationary vehicles in the forbidden area can be tracked. Our approach tested efficiency and robustness on a public database and is shown to produce state of the art results.