大型室内活动中获取的室内人员轨迹数据具有时空复杂性高、高维且不规则等特点,给可视分析带来了一定挑战。针对该问题,面向室内人员的时空模式、人群移动模式、异常行为模式等设计了一种基于兴趣区(AOI,area of interest)划分的室内轨...大型室内活动中获取的室内人员轨迹数据具有时空复杂性高、高维且不规则等特点,给可视分析带来了一定挑战。针对该问题,面向室内人员的时空模式、人群移动模式、异常行为模式等设计了一种基于兴趣区(AOI,area of interest)划分的室内轨迹可视分析方法 ,用户可自定义兴趣区并以此为单位进行室内轨迹分析,从而确定其时空模式、移动模式或异常行为。最后,使用China Vis2019挑战赛的数据验证了所提方法的有效性,达到了通过探索式分析室内人员轨迹获取有价值信息的目的。展开更多
We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language pr...We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language processing, we introduced a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyzed the global structural information of behaviors using their local action statistics. 2） The natural grouping of behavior patterns was discovered through a novel clustering algorithm. 3 ） A run-time accumulative anomaly measure was introduced to detect abnormal behavior, whereas normal behavior patterns were recognized when sufficient visual evidence had become available based on an online Likelihood Ratio Test （LRT） method. This ensured robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrated the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.展开更多
文摘大型室内活动中获取的室内人员轨迹数据具有时空复杂性高、高维且不规则等特点,给可视分析带来了一定挑战。针对该问题,面向室内人员的时空模式、人群移动模式、异常行为模式等设计了一种基于兴趣区(AOI,area of interest)划分的室内轨迹可视分析方法 ,用户可自定义兴趣区并以此为单位进行室内轨迹分析,从而确定其时空模式、移动模式或异常行为。最后,使用China Vis2019挑战赛的数据验证了所提方法的有效性,达到了通过探索式分析室内人员轨迹获取有价值信息的目的。
基金This work is supported by National Natural Science Foundation of China （NSFC） under Grant No. 60573139 andNational Science ＆ Technology Pillar Program of China under Grant NO. 2008BAH221303.
文摘We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language processing, we introduced a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyzed the global structural information of behaviors using their local action statistics. 2） The natural grouping of behavior patterns was discovered through a novel clustering algorithm. 3 ） A run-time accumulative anomaly measure was introduced to detect abnormal behavior, whereas normal behavior patterns were recognized when sufficient visual evidence had become available based on an online Likelihood Ratio Test （LRT） method. This ensured robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrated the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.
