Efficient and Fast Traffic Congestion Classification Based on Video Dynamics and Deep Residual Network

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Real-time implementation and robustness against illumination variation are two essential issues for traffic congestion classification systems, which are still challenging issues. This paper proposes an efficient automated system for traffic congestion classification based on compact image representation and deep residual networks. Specifically, the proposed system comprises three steps: video dynamics extraction, feature extraction, and classification. In the first step, we propose two approaches for modeling the dynamics of each video and produce a compact representation. In the first approach, we aggregate the optical flow in front direction, while in the second approach, we use a temporal pooling method to generate a dynamic image describing the input video. In the second step, we use a deep residual neural network to extract texture features from the compact representation of each video. In the third step, we build a classification model to discriminate between the classes of traffic congestion (low, medium, or high). We use the UCSD and NU1 traffic congestion datasets to assess the performance of the proposed method. The two datasets contain different illumination and shadow variations. The proposed method gives excellent results compared to state-of-the-art methods. It also can classify the input video in a short time (37 fps), and thus, we can use it with real-time applications.

本文言語英語
ホスト出版物のタイトルFrontiers of Computer Vision - 26th International Workshop, IW-FCV 2020, Revised Selected Papers
編集者Wataru Ohyama, Soon Ki Jung
出版社Springer
ページ3-17
ページ数15
ISBN(印刷版)9789811548178
DOI
出版ステータス出版済み - 2020
イベントInternational Workshop on Frontiers of Computer Vision, IW-FCV 2020 - Ibusuki, 日本
継続期間: 2 20 20202 22 2020

出版物シリーズ

名前Communications in Computer and Information Science
1212 CCIS
ISSN(印刷版)1865-0929
ISSN(電子版)1865-0937

会議

会議International Workshop on Frontiers of Computer Vision, IW-FCV 2020
国/地域日本
CityIbusuki
Period2/20/202/22/20

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 数学 (全般)

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