10 September 2013 Understanding vehicular traffic behavior from video: a survey of unsupervised approaches
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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.
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Brendan Tran Morris, Brendan Tran Morris, Mohan Trivedi, Mohan Trivedi, } "Understanding vehicular traffic behavior from video: a survey of unsupervised approaches," Journal of Electronic Imaging 22(4), 041113 (10 September 2013). https://doi.org/10.1117/1.JEI.22.4.041113 . Submission:
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