@inproceedings{46238787944743ee98adc855bdafbc81,
title = "ACSNET: ADAPTIVE CROSS-SCALE NETWORK WITH FEATURE MAPS REFUSION FOR VEHICLE DENSITY DETECTION",
abstract = "We investigate vehicle density detection from traffic surveillance. This task is rather challenging, mainly due to the low-resolution of data and large-scale variance of vehicles. The main result is that by learning cross-scale features, high-quality vehicle density maps can be attainable. Our main technical contribution is a learning model, called Adaptive Cross-Scale Network (ACSNet), that can learn cross-scale features from traffic surveillance data with low-resolution and large scale variance of vehicles. ACSNet consists of 1) a series of cross-scale feature extraction blocks with dense bypassing paths for harvesting spatial information, 2) an attention block for learning from appropriate scales, and 3) a structural similarity index for learning from occlusion scenes. We assess our ACSNet on two benchmark datasets, and extensive empirical evidence shows that our ACSNet performs favorably against the state-of-the-art methods.",
keywords = "Cross-Scale, Density Regression, Vehicle Count",
author = "Zuhao Ge and Yuhui Li and Cheng Liang and Youyi Song and Teng Zhou and Jing Qin",
note = "Funding Information: ∗Dr. Teng Zhou is the corresponding author. This work was supported in part to Dr. Teng Zhou by the NSFC (No. 61902232), the Natural Science Foundation of Guangdong Province (No. 2018A030313291), the Education Science Planning Project of Guangdong Province (2018GXJK048), the Guangdong Special Cultivation Funds for College Students{\textquoteright} Scientific and Technological Innovation (No. pdjh2020b0222), the STU Scientific Research Foundation for Talents (NTF18006), and the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG05D). Funding Information: This work was supported in part to Dr. Teng Zhou by the NSFC (No. 61902232), the Natural Science Foundation of Guangdong Province (No. 2018A030313291), the Education Science Planning Project of Guangdong Province (2018GXJK048), the Guangdong Special Cultivation Funds for College Students' Scientific and Technological Innovation (No. pdjh2020b0222), the STU Scientific Research Foundation for Talents (NTF18006), and the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG05D). Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Multimedia and Expo, ICME 2021 ; Conference date: 05-07-2021 Through 09-07-2021",
year = "2021",
month = jul,
doi = "10.1109/ICME51207.2021.9428454",
language = "English",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE International Conference on Multimedia and Expo, ICME 2021",
address = "United States",
}