Drone-Based Car Counting via Density Map Learning
- Resource Type
- Conference
- Authors
- Huang, Jingxian; Ding, Guanchen; Guo, Yujia; Yang, Daiqin; Wang, Sihan; Wang, Tao; Zhang, Yunfei
- Source
- 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Visual Communications and Image Processing (VCIP), 2020 IEEE International Conference on. :239-242 Dec, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Kernel
Automobiles
Training
Loss measurement
Feature extraction
Generators
Estimation
Car counting
density map learning
drone-based image
- Language
- ISSN
- 2642-9357
Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.