Vehicle Detecting and Tracking Application Based on YOLOv5 and DeepSort for Bayer Data

Recently, with the advancement of vehicle electronics hardware and the rapid development of artificial intelligence technology, intelligent transportation technology has begun to play a very significant role in the development of vehicle technology. Vehicle tracking technology based on deep learning has also received more and more attention, especially since the rapid development of deep neural networks. A significant contribution of this paper is an evaluation of the performance of neural networks in relation to the type of input data, and an exploration of future development possibilities. Based on YOLOv5 and DeepSort, a vehicle detection and tracking algorithm was chosen to test the algorithm's performance on the Bayer data. The results are discussed and analyzed.