计算机科学
跟踪(教育)
深度学习
人工神经网络
人工智能
关系(数据库)
车辆跟踪系统
技术开发
数码产品
实时计算
工程类
制造工程
卡尔曼滤波器
数据挖掘
电气工程
心理学
教育学
标识
DOI:10.1109/icarcv57592.2022.10004379
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI