Intelligent Recognition of Traffic Video Based on Mixture LDA Model

Xiaowei Tang; Xin-Lin Huang; Si-Yue Sun; Hang Dong; Xin Zhang; Yu Gao; Nan Liu

Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers

Research Article

Intelligent Recognition of Traffic Video Based on Mixture LDA Model

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@INPROCEEDINGS{10.1007/978-3-319-52730-7_36,
    author={Xiaowei Tang and Xin-Lin Huang and Si-Yue Sun and Hang Dong and Xin Zhang and Yu Gao and Nan Liu},
    title={Intelligent Recognition of Traffic Video Based on Mixture LDA Model},
    proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers},
    proceedings_a={MLICOM},
    year={2017},
    month={2},
    keywords={Bayesian model Mixture LDA model Traffic video identification},
    doi={10.1007/978-3-319-52730-7_36}
}

Xiaowei Tang
Xin-Lin Huang
Si-Yue Sun
Hang Dong
Xin Zhang
Yu Gao
Nan Liu
Year: 2017
Intelligent Recognition of Traffic Video Based on Mixture LDA Model
MLICOM
Springer
DOI: 10.1007/978-3-319-52730-7_36

Xiaowei Tang¹^,*, Xin-Lin Huang¹^,*, Si-Yue Sun²^,*, Hang Dong¹^,*, Xin Zhang¹^,*, Yu Gao¹^,*, Nan Liu¹

1: Tongji University
2: Shanghai Engineering Center for Micro-satellites

*Contact email: xwtang@tongji.edu.cn, xlhuang@tongji.edu.cn, sunmissmoon@163.com, dh@tongji.edu.cn, mic_zhangxin@tongji.edu.cn, gaoyu1631643@tongji.edu.cn

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.

Keywords: Bayesian model Mixture LDA model Traffic video identification

Published: 2017-02-24
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-52730-7_36