A Novel Multi-source Vehicle Detection Algorithm based on Deep Learning
Yong He, Liangqun Li · 2018
In this paper, a novel multi-source vehicle detection algorithm based on deep learning is proposed. In the proposed algorithm, in order to detect the vehicle objects, a fast deep learning algorithm based on convolutional neural network (CNN) is utilized to detect the vehicle objects, and the radar is used to obtain the position information about the vehicle objects. At the same time, a coordinate transformation method from radar coordinate system to video pixel coordinate system is presented, then the video detections and radar infromation are integrated to improve the detection performance of vehicles Finally, the experiment results based on the real datasets show that the proposed algorithm is very effective for the vehicle object detection and tracking.