A Crowd Anomaly Behavior Detection Algorithm

Autor:	Weiwei Xue, Guangrui Zhu, Cui Lijun, Facun Zhang
Rok vydání:	2018
Předmět:	Computer science business.industry Anomaly (natural sciences) media_common.quotation_subject Extraction algorithm Pattern recognition Motion (physics) Adaptability Feature (computer vision) Point (geometry) Artificial intelligence Abnormality business Decision model media_common
Zdroj:	2018 International Conference on Audio, Language and Image Processing (ICALIP).
DOI:	10.1109/icalip.2018.8455412
Popis:	This paper focuses on the detection of crowd abnormal behaviors in surveillance systems. By an improved Harris feature point extraction method in multi-scale space, feature points needed for crowd anomaly behavior detection are extracted. A feature point optimization method based on the motion foreground extraction algorithm is designed. The optimized feature points are classified according to the motion attributes. In the crowd abnormality behavior detection stage, a comprehensive abnormality decision method combining speed and direction is given. Experiment results shows this method has good adaptability in different scenarios.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6ed35debec780dd937bb93af3d3258e9 https://doi.org/10.1109/icalip.2018.8455412 Zobrazit plný text záznamu