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Time-Spatial Multiscale Net for Vehicle Counting and Traffic Volume Estimation
- Source :
- IEEE Transactions on Cognitive and Developmental Systems; 2022, Vol. 14 Issue: 2 p740-751, 12p
- Publication Year :
- 2022
-
Abstract
- Vehicle counting and traffic volume estimation on traffic videos are challenging tasks for traffic monitoring and management. Most previous methods are based on a process of vehicle detection and tracking or a process of time-spatial image (TSI)-based background subtraction, which suffer from occlusion and small size of vehicles. To overcome these difficulties, a time-spatial-based structure is proposed for vehicle counting and traffic volume estimation, without relying on tracking and TSI-based background subtraction. This structure has three main parts. First, a TSI-based density map generation model is proposed for generating density maps for TSIs, which makes it possible to automatically generate TSI training samples. Second, the time-spatial multiscale net (TM-Net) is proposed for estimating density map of TSI; with the stacked multiscale modules and the spatial attention module, the proposed TM-Net can partly overcome the difficulties brought from occlusion, small size, and deformation. Finally, a vehicle counting and traffic volume estimation model is designed for counting and volume estimation on the results of TM-Net. Experiments performed on the public UA-DETRAC data set show that the proposed TM-Net-based vehicle counting method outperforms the tested representative counting methods, and the proposed framework also can estimate traffic volume efficiently.
Details
- Language :
- English
- ISSN :
- 23798920
- Volume :
- 14
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Cognitive and Developmental Systems
- Publication Type :
- Periodical
- Accession number :
- ejs59937586
- Full Text :
- https://doi.org/10.1109/TCDS.2021.3073694