ACSNET: ADAPTIVE CROSS-SCALE NETWORK WITH FEATURE MAPS REFUSION FOR VEHICLE DENSITY DETECTION

Zuhao Ge, Yuhui Li, Cheng Liang, Youyi Song, Teng Zhou, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665438643
DOIs
Publication statusPublished - Jul 2021
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • Cross-Scale
  • Density Regression
  • Vehicle Count

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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