The Experts below are selected from a list of 159153 Experts worldwide ranked by ideXlab platform
Wenfong Hu - One of the best experts on this subject based on the ideXlab platform.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Junwei Hsieh - One of the best experts on this subject based on the ideXlab platform.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Sinuo Liu - One of the best experts on this subject based on the ideXlab platform.
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Particle Filter Vehicles Tracking by Fusing Multiple Features
IEEE Access, 2019Co-Authors: Yu Wang, Xiaojuan Ban, Huan Wang, Zixuan Wang, Yun Yang, Sinuo LiuAbstract:Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of Important Traffic parameters, which is the basis of the Traffic condition evaluation and the reasonable Traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.
Yungsheng Chen - One of the best experts on this subject based on the ideXlab platform.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Shihhao Yu - One of the best experts on this subject based on the ideXlab platform.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
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automatic Traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems, 2006Co-Authors: Junwei Hsieh, Shihhao Yu, Yungsheng Chen, Wenfong HuAbstract:This paper presents an automatic Traffic surveillance system to estimate Important Traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide Important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
