Detecting traffic anomalies is essential for diagnosing attacks. HighSp eed Backbone Net works （HSBN） require Traffic Anomaly Detection Systems （TADS） which are accurate （high detec tion and low false positive ra...Detecting traffic anomalies is essential for diagnosing attacks. HighSp eed Backbone Net works （HSBN） require Traffic Anomaly Detection Systems （TADS） which are accurate （high detec tion and low false positive rates） and efficient. The proposed approach utilizes entropy as traffic distributions metric over some traffic dimensions. An efficient algorithm, having low computational and space complexity, is used to estimate entro py. Entropy values over all dimensions are展开更多
In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two came...In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.展开更多
基金supported by the National High-Tech Research and Development Plan of China under Grant No.2011AA010702
文摘Detecting traffic anomalies is essential for diagnosing attacks. HighSp eed Backbone Net works （HSBN） require Traffic Anomaly Detection Systems （TADS） which are accurate （high detec tion and low false positive rates） and efficient. The proposed approach utilizes entropy as traffic distributions metric over some traffic dimensions. An efficient algorithm, having low computational and space complexity, is used to estimate entro py. Entropy values over all dimensions are
文摘In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.
