Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:Lect. Notes Comput. Sci.

Abstract:Violence detection in videos is of great importance in many applications, ranging from teenagers protection to online media filtering and searching to surveillance systems. Typical methods mostly rely on hand-crafted features, which may lack enough discriminative capacity for the specific task of violent action recognition. Inspired by the good performance of deep models for human action recognition, we propose a novel method for detecting human violent behaviour in videos by integrating trajectory and deep convolutional neural networks, which takes advantage of hand-crafted features [21] and deep-learned features [23]. To evaluate this method, we carry out experiments on two different violence datasets: Hockey Fights dataset and Crowd Violence dataset. The results demonstrate the advantage of our method over state-of-the art methods on these datasets. © 2017, Springer International Publishing AG.

ISSN No.:0302-9743

Translation or Not:no

Date of Publication:2017-01-01

Co-author:Meng, Zihan,李震

Correspondence Author:Meng, Zihan,Yuan Jiabing