Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may b...Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.展开更多
Piezocone penetration test(CPTu),the preferred in-situ tool for submarine investigation,is significant for soil classification and soil depth profile prediction,which can be used to predict soil types and states.Howev...Piezocone penetration test(CPTu),the preferred in-situ tool for submarine investigation,is significant for soil classification and soil depth profile prediction,which can be used to predict soil types and states.However,the accuracy of these methods needs to be validated for local conditions.To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta,seabed CPTu tests were carried out at ten stations in this area.Nine soil classification methods based on CPTu data are applied for soil classification.The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution.The methods presented by Robertson(based on soil behavior type index Ic),Olsen and Mitchell are the more consistent and compatible ones compared with other methods.Considering that silt soils have potential to liquefy under storm tide or other adverse conditions,this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.展开更多
A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion e...A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal's profile in the time domain. The features are extracted by the principal component analysis (PCA), which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate (IR) for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate (DR) and low false alarm rate (FAR) can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.展开更多
文摘Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.
基金The National Natural Science Foundation of China under contract Nos U2006213 and 41672272the Fundamental Research Funds for the Central Universities under contract No.201962011。
文摘Piezocone penetration test(CPTu),the preferred in-situ tool for submarine investigation,is significant for soil classification and soil depth profile prediction,which can be used to predict soil types and states.However,the accuracy of these methods needs to be validated for local conditions.To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta,seabed CPTu tests were carried out at ten stations in this area.Nine soil classification methods based on CPTu data are applied for soil classification.The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution.The methods presented by Robertson(based on soil behavior type index Ic),Olsen and Mitchell are the more consistent and compatible ones compared with other methods.Considering that silt soils have potential to liquefy under storm tide or other adverse conditions,this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.
基金National High Technology Research and Development Program of China (863 Program, Grant No. 2007AA01Z245), the supports provided for this research by the Major Program (Grant No. 61290312) and Youth Foundation (Grant No. 61301275) of the National Natural Science Foundation of China (NSFC), and the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2011J010). This work is also supported by Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT, IRT1218), and the 111 Project (B14039).
文摘A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal's profile in the time domain. The features are extracted by the principal component analysis (PCA), which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate (IR) for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate (DR) and low false alarm rate (FAR) can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.