In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
Video based surveillance systems have been widely used on freeway for traffic monitoring, as the cameras can provide the most intuitionistic information. In order to manage all the traffic videos automatically, in thi...Video based surveillance systems have been widely used on freeway for traffic monitoring, as the cameras can provide the most intuitionistic information. In order to manage all the traffic videos automatically, in this paper, a distributed real-time auto- surveillance system is presented. The freeway traffic videos are taken as input video from Pan Tilt Zoom （PTZ） camera, and then produces an analysis of the states and activity of the vehicles in the region of interested （ROI）, if there is any abnormal instance, an alarm and corresponding traffic video are sent to awake surveillants by Ethernet. To achieve this functionality, our system relies on three main procedures. The first one initializes the system. It detects the ROI of the scene, and performs the camera calibration to remove the perspective effect of the incoming image. The second one segments moving vehicles from the images, eliminate shadow and tracks them real-time. It uses a set of methods to obtain the background of the image, extracts the moving regions and tracks these moving regions by matching them between frames of the video sequence to obtain high-level information such as color, size, velocity, and trajectories of moving vehicles. In the third procedure, activities of vehicles are analyzed based on a series of preset situations which would happen on freeway. The detail information of each vehicle and the global statistical information are checked to find out any abnormal instance, and then triggered an alarm. We present details of the system, together with experiment results which demonstrate the accuracy and time responses.展开更多
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
文摘Video based surveillance systems have been widely used on freeway for traffic monitoring, as the cameras can provide the most intuitionistic information. In order to manage all the traffic videos automatically, in this paper, a distributed real-time auto- surveillance system is presented. The freeway traffic videos are taken as input video from Pan Tilt Zoom （PTZ） camera, and then produces an analysis of the states and activity of the vehicles in the region of interested （ROI）, if there is any abnormal instance, an alarm and corresponding traffic video are sent to awake surveillants by Ethernet. To achieve this functionality, our system relies on three main procedures. The first one initializes the system. It detects the ROI of the scene, and performs the camera calibration to remove the perspective effect of the incoming image. The second one segments moving vehicles from the images, eliminate shadow and tracks them real-time. It uses a set of methods to obtain the background of the image, extracts the moving regions and tracks these moving regions by matching them between frames of the video sequence to obtain high-level information such as color, size, velocity, and trajectories of moving vehicles. In the third procedure, activities of vehicles are analyzed based on a series of preset situations which would happen on freeway. The detail information of each vehicle and the global statistical information are checked to find out any abnormal instance, and then triggered an alarm. We present details of the system, together with experiment results which demonstrate the accuracy and time responses.
