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Dynamic Time Warping for Vehicle Classification
Halachkin, Aliaksei ; Honec, Peter (referee) ; Honzík, Petr (advisor)
This thesis focuses on the dynamic time warping. During the work was written C/Python library. Using this library, the algorithm was subsequently applied for the vehicle classification based on their shapes. Testing had been performed on real data from a laser scanner. Then the algorithm had been compared to the correlation and Euclidean distance. Finally, laboratory model had been created, which demonstrates vehicle recognition using dynamic time warping.
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Vehicle classification using inductive loops sensors
Halachkin, Aliaksei ; Klečka, Jan (referee) ; Honec, Peter (advisor)
This project is dedicated to the problem of vehicle classification using inductive loop sensors. We created the dataset that contains more than 11000 labeled inductive loop signatures collected at different times and from different parts of the world. Multiple classification methods and their optimizations were employed to the vehicle classification. Final model that combines K-nearest neighbors and logistic regression achieves 94\% accuracy on classification scheme with 9 classes. The vehicle classifier was implemented in C++.
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Vehicle classification using inductive loops sensors
Halachkin, Aliaksei ; Klečka, Jan (referee) ; Honec, Peter (advisor)
This project is dedicated to the problem of vehicle classification using inductive loop sensors. We created the dataset that contains more than 11000 labeled inductive loop signatures collected at different times and from different parts of the world. Multiple classification methods and their optimizations were employed to the vehicle classification. Final model that combines K-nearest neighbors and logistic regression achieves 94\% accuracy on classification scheme with 9 classes. The vehicle classifier was implemented in C++.
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Dynamic Time Warping for Vehicle Classification
Halachkin, Aliaksei ; Honec, Peter (referee) ; Honzík, Petr (advisor)
This thesis focuses on the dynamic time warping. During the work was written C/Python library. Using this library, the algorithm was subsequently applied for the vehicle classification based on their shapes. Testing had been performed on real data from a laser scanner. Then the algorithm had been compared to the correlation and Euclidean distance. Finally, laboratory model had been created, which demonstrates vehicle recognition using dynamic time warping.
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