The difference between the Chinese time view and the western time view originates from agricultural civilization＇s inertia and industrial civilization＇s feeling way. This time coordinate controls people＇s behavior,...The difference between the Chinese time view and the western time view originates from agricultural civilization＇s inertia and industrial civilization＇s feeling way. This time coordinate controls people＇s behavior, awareness of concepts, industrial relationship, interpersonal relationship and value orientation. Different cultures bred different time conceptions and time behaviors. This paper mainly talks about the aspects of their differences. When different time views are reflected in cross-cultural communication, conflicts and obstacles of interpersonal communication may arise. With the development of social economy and cultural communication, the time views of Chinese culture and western culture have impacts on each other and are gradually integrated with each other.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The difference between the Chinese time view and the western time view originates from agricultural civilization＇s inertia and industrial civilization＇s feeling way. This time coordinate controls people＇s behavior, awareness of concepts, industrial relationship, interpersonal relationship and value orientation. Different cultures bred different time conceptions and time behaviors. This paper mainly talks about the aspects of their differences. When different time views are reflected in cross-cultural communication, conflicts and obstacles of interpersonal communication may arise. With the development of social economy and cultural communication, the time views of Chinese culture and western culture have impacts on each other and are gradually integrated with each other.
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.
