%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2020/09.30.04.18
%2 sid.inpe.br/sibgrapi/2020/09.30.04.18.12
%@doi 10.1109/SIBGRAPI51738.2020.00041
%T Violence Detection and Localization in Surveillance Video
%D 2020
%A Choqueluque Roman, David Gabriel,
%A Cámara Chávez, Guillermo,
%@affiliation Department of Computer Science, Universidad Católica San Pablo, Arequipa, Perú
%@affiliation Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Porto de Galinhas (virtual)
%8 7-10 Nov. 2020
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Violence detection, violence localization, video surveillance, dynamic images, video summarization, saliency detection.
%X Automatic violence detection in video surveillance is crucial for social and personal security. Due to the massive video data produced by surveillance cameras installed in different environments like airports, trains, stadiums, schools, etc., traditional video monitoring by humans operators becomes inefficient. In this context, develop systems capable of detect automatically violent actions is a challenging task. This study describes a method to detect and localize violent acts in video surveillance using dynamic images, CNN's, and weakly supervised localization methods. Experimental results demonstrate the effectiveness of our approach when applied to three public benchmark datasets: Hockey Fight, Violent Flows, and UCFCrime2Local.
%@language en
%3 107.pdf