Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events intera...Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.展开更多
The objective of this work is to investigate the influence of smoke movement during mine fires on miner evacuation behaviors. A three-dimensional computational fluid dynamics method was conducted to reconstruct the la...The objective of this work is to investigate the influence of smoke movement during mine fires on miner evacuation behaviors. A three-dimensional computational fluid dynamics method was conducted to reconstruct the lane- way conveyor belt fire scenes under two ventilating conditions. The parameters, including temperature-time histories, soot density, carbon monoxide and heat release rate, were simulated to characterize the mine fires at various ventilating speeds. A miner evacuation model affected by fire smoke movement was advanced to describe the miner evacuation behaviors, which can be divided into three stages. Based on the evacuation model coupled with the mine fire smoke movement, the available safety evacuation time for miners involved in coal mine fire located in different sites was estimated. Two evacuation patterns were advanced according to the ventilating speeds combined with the model of miner evacuation behaviors. The results show that the miners located between the inlet-air end and the air door in lane 1 should be evacuated to the inlet-air end and other miners involved in coal mine fire could choose the air door as the escaping destination, when the ventilation speed is greater than 3 m/s. Accordingly, the research can be used as references for the mine safety administration authorities to design the safety evacuation.展开更多
基金Project(50808025)supported by the National Natural Science Foundation of ChinaProject(20090162110057)supported by the Doctoral Fund of Ministry of Education,China
文摘Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.
基金National Natural Science Foundation of China （51274205）, the Doctoral Program Foundation of Ministry of Education the New Teacher Project （20070290022） and the Open Project of China University of Mining and Technology Resources and Mine Safety State Key Laboratory （S KLCRSM 10KFB 13）.
文摘The objective of this work is to investigate the influence of smoke movement during mine fires on miner evacuation behaviors. A three-dimensional computational fluid dynamics method was conducted to reconstruct the lane- way conveyor belt fire scenes under two ventilating conditions. The parameters, including temperature-time histories, soot density, carbon monoxide and heat release rate, were simulated to characterize the mine fires at various ventilating speeds. A miner evacuation model affected by fire smoke movement was advanced to describe the miner evacuation behaviors, which can be divided into three stages. Based on the evacuation model coupled with the mine fire smoke movement, the available safety evacuation time for miners involved in coal mine fire located in different sites was estimated. Two evacuation patterns were advanced according to the ventilating speeds combined with the model of miner evacuation behaviors. The results show that the miners located between the inlet-air end and the air door in lane 1 should be evacuated to the inlet-air end and other miners involved in coal mine fire could choose the air door as the escaping destination, when the ventilation speed is greater than 3 m/s. Accordingly, the research can be used as references for the mine safety administration authorities to design the safety evacuation.
