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Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
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Image Segmentation with Deep Neural Network
Pazderka, Radek ; Šůstek, Martin (referee) ; Rozman, Jaroslav (advisor)
This master's thesis is focused on segmentation of the scene from traffic environment. The solution to this problem is segmentation neural networks, which enables classification of every pixel in the image. In this thesis is created segmentation neural network, that has reached better results than present state-of-the-art architectures. This work is also focused on the segmentation of the top view of the road, as there are no freely available annotated datasets. For this purpose, there was created automatic tool for generation of synthetic datasets by using PC game Grand Theft Auto V. The work compares the networks, that have been trained solely on synthetic data and the networks that have been trained on both real and synthetic data. Experiments prove, that the synthetic data can be used for segmentation of the data from the real environment. There has been implemented a system, that enables work with segmentation neural networks.
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Synthetic Dataset Generator for Traffic Analysis
Svoreň, Ondrej ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This bachelor thesis deals with the creation and customization of synthetic dataset genera tor for traffic analysis. It focuses on traffic analysis by means of computer vision, methods and conditions of creating the generator of synthetic dataset, possible application of achie ved results in machine learning and additional development opportunities. Using available automobile photographs from the Czech Republic, Slovakia, Poland and Hungary, a synthe tic license plate number generator was created, which, after graphical adjustment and after joining with the vehicle photographs creates the resulting dataset for machine learning. The solution itself is divided into the three scripts written in Python using the OpenCV library. The resulting dataset serves as an input for the machine learning system to re-identify the license plate numbers from photographs captured in the flow of traffic.
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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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Comparison of technologies for vehicle passage detection
Mareková, Martina ; Frolka, Jakub (referee) ; Krajsa, Ondřej (advisor)
System for vehicle detection and speed measurement system is an important part in traffic control as well as in providing data for intelligent and automated traffic signs. These technologies are divided into two types: intrusive and non-intrusive methods. Traditional traffic control is based on inductive loops. This method is intrusive and therefore requires intervention under the road and higher maintenance requirements. We can avoid this problem with non-intrusive methods, including video analysis, microwave radars, geomagnetic radars, weighing sensors and laser sensors, but these are more expensive alternatives. By implementing radars we performed measurements on road sections, on the basis of which the output data from microwave, weigh-in-motion sensors and induction loops were displayed. We receive processed and unprocessed output data of measurements from each sensors. For processing of those data is created software solution for graph rendering and direct measurement of the sensor accuracy in comparison with competing technology. The results of the measurements showed that the efficiency of the individually implemented sensors is sufficient for use in traffic control. As well we can consider the implementation of microwave radars and cameras for video analysis as a replacement for the conventional method of induction loops.
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Traffic Monitoring from Aerial Video Data
Babinec, Adam ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis proposes a system for extraction of vehicle trajectories from aerial video data for traffic analysis. The system is designed to analyse video sequence of a single traffic scene captured by an action camera mounted on an arbitrary UAV flying at the altitudes of approximately 150 m. Each video frame is geo-registered using visual correspondence of extracted ORB features. For the detection of vehicles, MB-LBP classifier cascade is deployed, with additional step of pre-filtering of detection candidates based on movement and scene context. Multi-object tracking is achieved by Bayesian bootstrap filter with an aid of the detection algorithm. The performance of the system was evaluated on three extensively annotated datasets. The results show that on the average, 92% of all extracted trajectories are corresponding to the reality. The system is already being used in the research to aid the process of design and analysis of road infrastructures.
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Reconstruction of 3D Information about Vehicles Passing in front of a Surveillance Camera
Dobeš, Petr ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This master's thesis focuses on 3D reconstruction of vehicles passing in front of a traffic surveillance camera. Calibration process of surveillance camera is first introduced and the relation of automatic calibration with 3D information about observed traffic is described. Furthermore, Structure from Motion, SLAM, and optical flow algorithms are presented. A set of experiments with feature matching and the Structure from Motion algorithm is carried out to examine results on images of passing vehicles. Afterwards, the Structure from Motion pipeline is modified. Instead of using SIFT features, DeepMatching algorithm is utilized to obtain quasi-dense point correspondences for the subsequent reconstruction phase. Afterwards, reconstructed models are refined by applying additional constraints specific to the vehicle reconstruction task. The resultant models are then evaluated. Lastly, observations and acquired information about the process of vehicle reconstruction are utilized to form proposals for prospective design of an entirely custom pipeline that would be specialized for 3D reconstruction of passing vehicles.
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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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Cloud Application for Traffic Analysis
Valchář, Vít ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
The aim of this thesis is to create a cloud application for traffic analysis without knowing anything about the system. The only input is address of the web camera pointing at traffic. This application is build on existing solution which is further enhanced. New modules for removing obstacles (such as lamppost covering part of the road) and splitting overlapping cars were added. The whole cloud solution consists of multiple components which communicates by HTTP messages and are controlled by web interface.
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Comparison of technologies for vehicle passage detection
Mareková, Martina ; Frolka, Jakub (referee) ; Krajsa, Ondřej (advisor)
System for vehicle detection and speed measurement system is an important part in traffic control as well as in providing data for intelligent and automated traffic signs. These technologies are divided into two types: intrusive and non-intrusive methods. Traditional traffic control is based on inductive loops. This method is intrusive and therefore requires intervention under the road and higher maintenance requirements. We can avoid this problem with non-intrusive methods, including video analysis, microwave radars, geomagnetic radars, weighing sensors and laser sensors, but these are more expensive alternatives. By implementing radars we performed measurements on road sections, on the basis of which the output data from microwave, weigh-in-motion sensors and induction loops were displayed. We receive processed and unprocessed output data of measurements from each sensors. For processing of those data is created software solution for graph rendering and direct measurement of the sensor accuracy in comparison with competing technology. The results of the measurements showed that the efficiency of the individually implemented sensors is sufficient for use in traffic control. As well we can consider the implementation of microwave radars and cameras for video analysis as a replacement for the conventional method of induction loops.
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