JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Abnormal Behavior Recognition Based on Spatio-temporal Context |
| 영문제목(English Title) |
Abnormal Behavior Recognition Based on Spatio-temporal Context |
| 저자(Author) |
Yuanfeng Yang
Lin Li
Zhaobin Liu
Gang Liu
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| 원문수록처(Citation) |
VOL 16 NO. 03 PP. 0612 ~ 0628 (2020. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects’ behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.
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| 키워드(Keyword) |
Abnormal Behavior Recognition
Cascade Model
Spatio-temporal Context
Topic Model
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| 파일첨부 |
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