An approach to detect crowd panic behavior using flow-based feature

Abstract

With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.