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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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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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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 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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Detection of the Cars Approaching the Crossroad
Vácha, Lukáš ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
Traffic monitoring using computer vision is becoming the desired system in practice. It allows nondestructive installation and also is very useful in many applications. This thesis focuses on automatic detection of vehicles approaching to a crossroads. This work also includes description of selected methods for detecting moving vehicles and the way of tracking them. On the basis of these methods is designed application that is implemented and tested in different lighting and weather conditions and various direction of approaching vehicles.
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Detection of Vehicles in Image
Špaňhel, Jakub ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This thesis aims to implement the vehicle detection and tracking method based on the motion model suitable for real-time processing. The first part includes analysis of the commonly used methods. The second part introduces principles of implemented method. This method consists of low-level features extraction, the spatiotemporal profiling of extracted features and image intensities, and classification of obtained traces based on HMM. Subsequently experiments using this trustworthy method are conducted to locate areas of potential method improvements.
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SMART CAR: Automatic Car Detection
Burkot, Martin ; Žák, Pavel (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis deals with the detection of moving vehicles in image sequence. In the introduction is made a brief analysis of current methods for detecting the movement of vehicles and the scene in general. In subsequent chapters is designed and described the implementation of the detector moving vehicles in an image based on the determination of optical flow. At the end there review of proposed solution.
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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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Traffic image sequence classification
Vomela, Miroslav ; Janáková, Ilona (referee) ; Honec, Peter (advisor)
The article introduces a general survey of concepts used in traffic monitoring applications. It describes different approaches for solving particular steps of vehicle detection process. Analysis of these methods was performed. Furthermore this work focuses on the design and realization of complex robust algorithm for real-time vehicle detection. It is based on analysis of video-sequence acquired from static camera situated on highway. Processing consists of many steps. It starts with background subtraction and ends with traffic monitoring results, i.e. average speed, number of cars, level of service etc.
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Videodetection - traffic monitoring
Kozina, Lubomír ; Beszédeš,, Marián (referee) ; Honec, Peter (advisor)
In this master’s thesis on the topic Videodetection - traffic monitoring I was engaged in searching moving objects in traffic images sequence. There are described various methods background model computation and moving vehicles marking, counting or velocity calculating in the thesis. It was created a graphical user interface for traffic scene evaluation in MATLAB.
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