1. A Fast Vehicle Counting and Traffic Volume Estimation Method Based on Convolutional Neural Network
- Author
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Hailong Meng, Shuang Li, Xianglin Dai, Henglong Yang, Yu Zhang, and Youmei Zhang
- Subjects
Estimation ,Vehicle counting ,General Computer Science ,Artificial neural network ,Computer science ,Real-time computing ,Feature extraction ,General Engineering ,traffic volume estimation ,Convolutional neural network ,time-spatial image ,TK1-9971 ,Image (mathematics) ,Task (computing) ,density map ,Trajectory ,General Materials Science ,Electrical engineering. Electronics. Nuclear engineering ,attention mechanism - Abstract
Vehicle counting and traffic volume estimation on traffic videos has gained extensive attention from multimedia and computer vision communities. Recent vehicle counting and volume estimation methods, including detection based and time-spatial image (TSI) based methods have achieved significant improvements. However, how to balance the accuracy and speed is still a challenge to this task. In this paper, we design a fast and accurate vehicle counting and traffic volume estimation method. Firstly, traffic videos are converted to TSIs and we annotate the vehicle locations in TSIs manually. Then, we design a simple TSI density map estimation network which utilizes attention mechanism to strengthen the features in the traffic locations for vehicle counting. Finally, we use the parameters obtained from the vehicle counting network to further estimate the traffic volume. Experiments on UA-DETRAC dataset demonstrate that the vehicle counting network not only takes a balance between counting accuracy and speed, but also well estimates the traffic volume when the video data is insufficient.
- Published
- 2021