Anomaly detection and localisation in the crowd scenes using a block‐based social force model. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Ji, Qing‐Ge; Chi, Rui; Lu, Zhe‐Ming
• Is Part Of: IET image processing. Volume 12:Issue 1(2018)
• Page Start: 133
• Page End: 137
• Publication Date: 2018-01-01
• Subjects: natural scenes -- video surveillance -- Gaussian processes -- mixture models -- pedestrians -- social sciences computing; block‐based social force model -- anomalous event localisation -- anomalous event detection -- crowed scenes -- surveillance video processing -- pixel‐level anomaly detection -- block‐level anomaly detection -- Gaussian mixture models -- pedestrian detection -- Ped1 USCD dataset -- Ped2 USCD dataset; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0044
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A novel approach to detect and localise anomalous events in crowed scenes by processing surveillance videos is introduced in this study. Unusual events are those that significantly differ from current dominated behaviours. The proposed approach both detects pixel‐level and block‐level anomalies. In pixel level, Gaussian mixture models are used to detect abnormalities. Block‐level detection segments the crowd into blocks according to pedestrian detection, and then anomalies are spotted and localised with a social force model. Experimental results using the USCD datasets Ped1 and Ped2 show that the proposed method performs favourably against state‐of‐the‐art methods.