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Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance
Author(s) -
Xu Xianghua,
Liu LiQiming,
Zhang Lingjun,
Li Ping,
Chen Jinjun
Publication year - 2020
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2701
Subject(s) - computer science , artificial intelligence , event (particle physics) , feature (linguistics) , computer vision , enhanced data rates for gsm evolution , autoregressive model , object detection , optical flow , pattern recognition (psychology) , image (mathematics) , linguistics , philosophy , physics , quantum mechanics , economics , econometrics
Summary The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.