融合显著性信息和社会力模型的人群异常检测  

Abnormal Crowd Event Detection by Fusing Saliency Information and Social Force Model

作　　者：李萌[1] 陈恳[1] 郭春梅[1] 李斐[1] 吉培培 

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《光电工程》2016年第12期193-199,共7页Opto-Electronic Engineering

基　　金：国家自然科学基金(60972063);宁波市自然科学基金(2014A610065);宁波大学科研基金(理)/学科项目(XKXL1308)

摘　　要：人群异常事件检测是智能视频监控中的重要研究内容,本文提出一种新的融合时空特征的异常行为检测算法。首先提取显著性信息作为空间域特征,采用高精度的光流算法,结合社会力模型计算相互作用力作为时域特征;提出一种新的运动信息特征描述子——相互作用力直方图(HOIF),将其与显著性信息特征相融合送入支持向量机(SVM)进行学习训练,从而对人群事件进行分类。在UMN(University of Minnesota,Twin Cities)数据库上对本文算法有效性进行了验证。实验结果表明,该算法在检测正确率及鲁棒性上要优于其他算法。Abnormal event detection plays an important role in intelligent video surveillance. A new abnormal behavior detection algorithm is presented by fusing spatiotemporal features. We first extract SI as the feature representation in the spatial domain. Then, by combining the high precision optical flow algorithm with social force model, we calculate the interaction force as the feature representation in the temporal domain. A novel motion feature descriptor, i.e., Histogram of Interaction Force （HOIF） is proposed, which is combined with SI as temporal-spatial features to be input to the Support Vector Machine （SVM） to identify the crowd events. The effectiveness of the proposed algorithm is put to test on the UMN dataset, and the experimental results indicate that the presented method offers more reliable performance than some existing algorithms in terms of accuracy and robustness.

关 键 词：时空特征 显著性信息 社会力模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]