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Fine-Grained Recognition and Re-Identification of Vehicles Using Advanced Feature Extraction
Doseděl, Ondřej ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
Práce se zabývala analýzou a následným vylepšením metod užívaných k rozpoznávání typů vozidel a jejich re-identifikace. Navržená metoda může být využita jak pro rozeznání, tak pro re-identifikaci. Byla založena na používání tzv. 3D bounding boxes. Pomocí těchto boxů docházelo k detekci vozidla na obraze. Vozidlo bylo následně normováno rozbalením do dvojrozměrné interpretace. Tato metoda byla vylepšena určením směru vozidla a rozlišováním mezi čelní a zadní stranou vozidla během rozbalení třírozměrného modelu. Představená metoda vylepšuje stávající metodu pro rozpoznávání a snižuje její chybovost až o 13 % pro jeden vzorek a o 17% pro přesnost trati. Pro re-identifikaci nedošlo k zlepšení při použití LFTD agregovaní.
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Re-Identification of Vehicles by License Plate Recognition
Špaňhel, Jakub ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.
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Vehicle Re-Identification Using Vision Transformers
Jelínek, Zdeněk ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The main objective of this thesis was to investigate the potential of vision transformers in vehicle re-identification. Convolutional neural networks have so far dominated this area of computer vision. In total, two models have been tested - TransReID and CMT. TransReID is a model based purely on vision transformers and was created specifically for vehicle re-identification. The main part of the experiments with this model was devoted to the use of key points on the vehicle. With proper extraction of the regions around the key points and the use of post-processing, I achieved state-of-the-art results. The CMT model is a combination of convolutional networks and transformers that was not designed for vehicle re-identification. I modified the model and conducted extensive experiments with it to obtain the best configuration for vehicle re-identification. I evaluated the models on the standard datasets VeRi-776, VehicleID, CityFlowV2-ReID and CarsReId74k and compared with state-of-the-art models. With the CMT model, I achieved the best result of 0.860 on the mAP metric on the VeRi-776 dataset and the best result of 97.6% on the Rank5 metric on the VehicleID dataset.
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Fine-Grained Recognition and Re-Identification of Vehicles Using Advanced Feature Extraction
Doseděl, Ondřej ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
Práce se zabývala analýzou a následným vylepšením metod užívaných k rozpoznávání typů vozidel a jejich re-identifikace. Navržená metoda může být využita jak pro rozeznání, tak pro re-identifikaci. Byla založena na používání tzv. 3D bounding boxes. Pomocí těchto boxů docházelo k detekci vozidla na obraze. Vozidlo bylo následně normováno rozbalením do dvojrozměrné interpretace. Tato metoda byla vylepšena určením směru vozidla a rozlišováním mezi čelní a zadní stranou vozidla během rozbalení třírozměrného modelu. Představená metoda vylepšuje stávající metodu pro rozpoznávání a snižuje její chybovost až o 13 % pro jeden vzorek a o 17% pro přesnost trati. Pro re-identifikaci nedošlo k zlepšení při použití LFTD agregovaní.
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Re-Identification of Vehicles by License Plate Recognition
Špaňhel, Jakub ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.
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