复杂公共环境下群体行为尺度自适应建模与特定异常行为识别算法研究

项目名称： 复杂公共环境下群体行为尺度自适应建模与特定异常行为识别算法研究

项目编号： No.61501060

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 杨彪

作者单位： 常州大学

项目金额： 19万元

中文摘要： 群体异常行为的准确检测是智能视频分析系统功能发挥的一个重要基础，但复杂公共环境会影响群体行为建模的准确性，从而降低异常行为的检测率。同时，传统异常行为检测算法不能识别特定群体异常行为，导致无法对严重异常行为优先响应。针对这些问题，本项目首先提取群体行为的多种特征进行联合分析，根据特征分析结果估计当前场景的人群拥挤程度，并基于拥挤程度设计尺度自适应的群体行为模型，有效提高了复杂公共环境下群体行为建模的准确性；然后，设计分层分类器识别特定群体异常行为，第一层分类器用于检测广义群体异常行为，第二层分类器用于识别特定群体异常行为；最后，在真实监控环境下搭建群体异常行为检测平台，验证并改进所提算法的性能。本项目的创新之处在于从新的角度考虑了影响群体行为模型准确性与异常行为检测实用性的关键因素，将有效解决复杂公共环境中难以识别特定群体异常行为的难题，具有重要的理论与应用价值。

中文关键词： 异常行为识别；群体行为建模；复杂公共环境；多特征联合分析；尺度自适应

英文摘要： Accurate detection of crowd abnormal behaviors is one of the essential foundations that can influence the function effectiveness of intelligent video analysis. However, complex public environment may impact the accuracy of crowd behaviors modeling which leads to a decrement in the detection rate of abnormal behaviors. Meanwhile, due to the lack of identification ability of identifying the specific crowd abnormal behaviors utilizing traditional abnormal behaviors detection algorithm, severely abnormal behaviors cannot be answered prior to others. To handle these problems, multiple features of the crowd behaviors are extracted initially for conjoint analysis and congestion level is estimated based upon the analysis result. Then given the estimated congestion level, the scale adaptive crowd behaviors model is constructed to improve the accuracy of crowd behaviors modeling under complex public environment. After that, a hierarchical classifier is designed to identify the specific crowd abnormal behaviors. The first layer of the classifier is used to detect the general crowd abnormal behaviors and the second layer is utilized to identify the specific crowd abnormal behaviors. Finally, the effectiveness of the algorithm is demonstrated and reinforced through performing experiments on the constructed crowd abnormal behaviors detection platform under practical monitoring environment. Novelty of the project lies on the proposed algorithm which takes into account the key factors affecting the accuracy of crowd behaviors model and the effectiveness of abnormal behaviors detection from a new perspective. Therefore, this project will have significant theoretical and practical value to effectively identify specific crowd abnormal behaviors under complex public environment.

英文关键词： Abnormal Behaviors Identification ;Crowd Behaviors Modeling ;Complex Public Environment;Multiple Features Conjoint Analysis; Scale Adaptive