Pedestrian Behavior Modeling From Stationary Crowds With Applications to Intelligent Surveillance
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AbstractPedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance. Stationary crowd groups are a key factor influencing pedestrian walking patterns but was mostly ignored in the literature. It plays different roles for different pedestrians in a crowded scene and can change over time. In this paper, a novel model is proposed to model pedestrian behaviors by incorporating stationary crowd groups as a key component. Through inference on the interactions between stationary crowd groups and pedestrians, our model can be used to investigate pedestrian behaviors. The effectiveness of the proposed model is demonstrated through multiple applications, including walking path prediction, destination prediction, personality attribute classification, and abnormal event detection. To evaluate our model, two large pedestrian walking route datasets are built. The walking routes of around 15 000 pedestrians from two crowd surveillance videos are manually annotated. The datasets will be released to the public and benefit future research on pedestrian behavior analysis and crowd scene understanding.
All Author(s) ListYi S, Li HS, Wang XG
Journal nameIEEE Transactions on Image Processing
Volume Number25
Issue Number9
Pages4354 - 4368
LanguagesEnglish-United Kingdom
Keywordscrowd video surveillance; Pedestrian behavior modeling; stationary crowd groups
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-17-10 at 00:49