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Employing topical relations in semantic analysis of traffic videos

Ahmadi, P ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/MIS.2018.111144040
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos. © 2001-2011 IEEE
  6. Keywords:
  7. Analytical models ; Artificial intelligence ; Correlation methods ; Image analysis ; Intelligent systems ; Object recognition ; Optical data processing ; Semantics ; Time and motion study ; Traffic control ; Computing methodologies ; Event detection ; Image processing and computer vision ; Videos ; Vision and scene understanding ; Video signal processing
  8. Source: IEEE Intelligent Systems ; Volume 34, Issue 1 , 2019 , Pages 3-13 ; 15411672 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8255788