Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Abstract

Vehicle counting is important for smart city applications such as logistics management, traffic estimation, and financial analysis. To perform vehicle counting using aerial images, researchers have proposed many algorithms, including detection-based, regression-based and density-based methods. However, most of these algorithms are only applicable to high-resolution images, which require clear vehicle outlines. For the reasons of acquisition difficulty, frequency and cost, it is necessary to explore methods for vehicle counting using low-resolution or even very low-resolution images. We build a cross-resolution vehicle counting (CRVC) dataset, including 192 very low-resolution images and 8 high-resolution images of a port from 2016 to 2019. For this task, we propose a novel vehicle counting via cross-resolution spatial consistency and intra-resolution time continuity constraints. The segmentation map is first obtained by semantic segmentation with the prior information above. The vehicle coverage rate relative to the located parking lot is calculated and then converted to vehicle area. Finally, the relationship between the area and the number of vehicles is established by regression. Experiments show that the vehicle counting results obtained by our method are highly consistent with the annotations and outperform other state-of-the-art methods. Our method is also applicable for images with a lower resolution of 10m and other locations. Code, data and pre-trained models are available online at https://github.com/hbsszq/Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images.