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Journal of Electronic Imaging

Understanding vehicular traffic behavior from video: a survey of unsupervised approaches
Author(s): Brendan Tran Morris; Mohan Trivedi
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Paper Abstract

Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provided.

Paper Details

Date Published: 10 September 2013
PDF: 16 pages
J. Electron. Imag. 22(4) 041113 doi: 10.1117/1.JEI.22.4.041113
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Brendan Tran Morris, Univ. of Nevada, Las Vegas (United States)
Mohan Trivedi, Univ. of California, San Diego (United States)

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