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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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Fine-Grained Vehicle Recognition from Traffic Surveillance Camera
Mencner, Pavel ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
The aim of this thesis is image based detection of vehicles from traffic surveillance camera and fine-grained vehicle type recognition (manufacturer and model). In the thesis the Unpack normalization method is implemented which transforms the vehicle image into its apparent flat representation in order to increase the classifier's success rate. The Unpack method make use of 3D bounding box of the vehicle. This bounding box is constructed during test period using the information of vehicle contour and direction toward vanishing points. The thesis involve accuracy comparison between direct and Unpack classification methods. The proposed solution is based on several related parts that benefit from convolutional neural networks. These parts are: vehicle detection from image data, estimation of the directions towards vanishing points solved as classification task, vehicle contour detection using convolutional Encoder-Decoder network and fine-grained vehicle type classification. Using Unpack based classification the 2% accuracy improvement against direct classification has been achieved, resulting in 86% overall success rate. The outcome of this thesis is fine-grained vehicle classification system that works with traffic surveillance video without any viewpoint limitations.
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Vehicle Collision Detection
Kruták, Martin ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This bachelor thesis decribes a system for detection and tracking of multiple vehicles from a surveillance camera with a collision detection. The focus is on detection and prediction of collisions of vehicles in one direction - towards the camera. System is not fully automatic, meaning that some initial settings are needed (e.g. lines on the road) to quarantee a good functionality of the system. Accurate vehicles' contour is obtained in the detection phase, and object centroids are calculated. Each detected vehicle is assigned to the specific lane and tracked separately. This thesis then describes the method of prediction and detection of a collision. A rectangle is created around the ground part of every vehicle. This rectangle of each of the vehicles is enlarged and checked for the overlaps. Those rectangles that overlaps are then subject to further analysis for the collision detection. Experimental results show a success rate of 72 % for the accurate rectangle construction being a crucial part for the collision detection. The advantage of the proposed system is its possible usage in surveillance cameras monitoring the traffic flow on highways.
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Vehicle detection in images
Pálka, Zbyněk ; Přinosil, Jiří (referee) ; Krajsa, Ondřej (advisor)
This thesis dissert on traffic monitoring. There are couple of different methods of background extraction and four methods vehicle detection described here. Furthermore there is one method that describes vehicle counting. All of these methods was realized in Matlab where was created graphical user interface. One whole chapter is dedicated to process of practical realization. All methods are compared by set of testing videos. These videos are resulting in statistics which diagnoses about efficiency of single one method.
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Vehicle Location Detection and Distribution from Camera Images
Stryk, Filip ; Götthans, Jakub (referee) ; Götthans, Tomáš (advisor)
Tato bakalářská práce se zabývá detekcí a sledováním polohy vozidel. Nejdříve jsou představeny základní principy hlubokého učení a konvolučních neuronových sítí. Jsou popsány detektory objektů fungující na principu konvolučních neuronových sítí se zaměřením především na YOLO, které jsou následně porovnány z hlediska přesnosti a rychlosti. Je navržen, implementován a vyhodnocen systém pro detekci a sledování polohy vozidel s pomocí YOLOv4-tiny a SORT.
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Server for Data Communication between Drones
Herrgott, Jiří ; Materna, Zdeněk (referee) ; Bambušek, Daniel (advisor)
This master thesis focuses on the transfer of flight and multimedia data between operators and drones for better real-time mission planning and coordination. A program has been developed that primarily provides these services. The work emphasizes robustness, reusability and extensibility, allowing the creation of modules such as detection or 3D reconstruction from the transmitted data. In addition, modules for flight and multimedia data storage and a module for vehicle detection from shared images have been developed in this work. Experiments were performed to evaluate the response time, the required computational resources and the used connection bandwidth.
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Vehicle Speed Measurement Using Stereo Camera Pair
Najman, Pavel ; Sojka, Eduard (referee) ; Guillemaut, Jean-Yves (referee) ; Zemčík, Pavel (advisor)
Tato práce se snaží najít odpověď na otázku, zda je v současnosti možné autonomně měřit rychlost vozidel pomocí stereoskopické měřící metody s průměrnou chybou v rozmezí 1 km/h, maximální chybou v rozmezí 3 km/h a směrodatnou odchylkou v rozmezí 1 km/h. Tyto rozsahy chyb jsou založené na požadavcích organizace OIML, jejichž doporučení jsou základem metrologických legislativ mnoha zemí. Pro zodpovězení této otázky je zformulována hypotéza, která je následně testována. Metoda, která využívá stereo kameru pro měření rychlosti vozidel je navržena a experimentálně vyhodnocena. Výsledky pokusů ukazují, že navržená metoda překonává výsledky dosavadních metod. Průměrná chyba měření je přibližně 0.05 km/h, směrodatná odchylka chyby je menší než 0.20 km/h a maximální absolutní hodnota chyby je menší než 0.75 km/h. Tyto výsledky jsou v požadovaném rozmezí a potvrzují tedy testovanou hypotézu.
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Object detection in video using neural networks
Mikulský, Petr ; Sikora, Pavel (referee) ; Myška, Vojtěch (advisor)
This diploma thesis deals with the detection of moving objects in a video recording using neural networks. The aim of the thesis was to detect road users in video recordings. Pre-trained YOLOv5 object detection model was used for a practical part of the thesis. As part of the solution, an own dataset of traffic road video recordings was created and annotated with following classes: a car, a bus, a van, a motorcycle, a truck and a trailer truck. Final version of this dataset comprise 5404 frames and 6467 annotated objects in total. After training, the YOLOv5 model achieved 0.995 mAP, 0.995 precision and 0.986 recall on the dataset. All steps leading to the final form of the dataset are described in the conclusion chapter.
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Detection of Vehicles in Image and Video
Petráš, Adam ; Zemčík, Pavel (referee) ; Špaňhel, Jakub (advisor)
This bachelor thesis is focused on vehicle detection. The thesis deals with the method of vehicle detection using convolutional neural networks, their structures and models. All scripts were implemented using python programming language with Tensorflow Object Detection API interface. The first part of this thesis was devote to the structures of popular neural networks and models of detection neural networks. The next chapter deals with the most famous frameworks that are used for machine learning. Three neural network models were selected and trained on the COD20K dataset. The result of this thesis is statistics that discuss the efficiency and performance of each model on trained dataset and compare performance without displaying video on Nvidia RTX 2060, where the performace archieved by SSD MobileNet V2 network was 300FPS and Nvidia Tegra TX2 8GB, whose performace reached almost 44FPS.
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Traffic assistant system for complicated situations
Podola, David ; Janáková, Ilona (referee) ; Petyovský, Petr (advisor)
T-intersections are one of the most common places where collisions happen. An intelligent traffic mirror is one the possible solutions to reduce the accident rate. The mirror detects the situation around the intersection, process the data and provides the driver with an information, whether the situation is safe and the driver can enter the junction safely. The aim of the thesis is a feasibility study of reliable detection of non-stationary objects based on cameras. The core of the intended product – the detection algorithm – detected the object on short distance from the camera reliably but as the distance was growing, the detection quality degraded. One of the possible solutions to achieve better detection results on longer distances may be achieved by using a camera with greater zoom. Based on the example improvement proposal, the feasibility of the solution based on optical methods was finally confirmed.
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