Abstract
In this paper, an efficient unsupervised model is proposed to recognize simple actions and complex activities in traffic scenes which is named mixture LDA model. Under this framework, we use hierarchical Bayesian models are to describe three important components in traffic video: basic visual features, simple actions, and complex activities. This model adopts an unsupervised way to learn how to recognize traffic video. Moving pixels can be divided into different simple actions and short video clips can be divided into different complex activities in a long traffic video sequence, then we can achieve the purpose of recognizing different activities in the surveillance video.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tang, X. et al. (2017). Intelligent Recognition of Traffic Video Based on Mixture LDA Model. In: Xin-lin, H. (eds) Machine Learning and Intelligent Communications. MLICOM 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-319-52730-7_36
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DOI: https://doi.org/10.1007/978-3-319-52730-7_36
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