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Tracking people based on their clothing in multi-camera systems
Sivak, Mykyta ; Přinosil, Jiří (referee) ; Číka, Petr (advisor)
This bachelor thesis focuses on the development and implementation of an algorithm for tracking individuals in multi-camera systems based on clothing pattern analysis. The aim was to design a system capable of tracking an individual in various positions and frames, using the Region of Interest (RoI) technique. The study begins with a comprehensive review of the existing literature on object tracking in video sequences, with a special focus on RoI tracking techniques. During the research, a new algorithm was developed and implemented that utilizes clothing patterns as the primary identification element for tracking and re-identifying individuals across different camera shots. The algorithm was experimentally validated on datasets containing video sequences from various environments, allowing for a detailed analysis of its effectiveness and reliability. The experimental results demonstrate that the proposed system achieves significant accuracy and efficiency compared to traditional methods and is particularly effective in challenging situations where other methods fail. The thesis concludes with an evaluation of the conducted experiments along with recommendations for future extensions and improvements of the system. Potential challenges and ethical aspects, including issues of privacy and personal data processing, are also discussed.
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Detection of Nudity in an Image Data
Pešková, Daniela ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Zameranie tejto práce je vytvorenie nástroja schopného detekovať nahotu v obrazových dátach. To je dosiahnuté natrénovaním modelu na detekciu inkriminovaných častí tela a vytvorením algoritmu schopného detekovať pokožku. Výsledné nástroje môžu byť použité pre automatickú detekciu nahoty v obrázkoch. Prvá časť práce sa zameriava na teóriu neurónových sietí a počítačového videnia so zameraním na detekciu pokožky. Druhá časť hovorí o prístupe zvolenom pre vytvorenie datasetu, procese tvorby a trénovania modelu schopného detekovať nahotu v obraze, ako aj o algoritmickom prístupe.
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Decreased visibility and image defect detection for vehicle mounted camera
Sedláček, Miloš ; Řičánek, Dominik (referee) ; Svědiroh, Stanislav (advisor)
This bachelor thesis deals with the topic of decreased visibility and image defects detection caused by adverse weather conditions or lighting from a vehicle mounted camera. The thesis describes the basic characteristics of the most common influences and their effects on camera data and presents some existing methods of detecting these influences. Next, a dataset containing selected defects is created and described. Afterwards, the issue of artificial neural networks is described in the thesis. A convolutional neural network is implemented for defect detection, which is trained and tested using the dataset. At the end, the achieved results of the network, its computational complexity and comparison with the results of other works are presented.
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Lane detection for autonomous vehicles
Holík, Štěpán ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This thesis focuses on the design and experimental verification of a system for lane detection, trajectory estimation and vehicle position. The goal was to develop a system composed of algorithms with its respective functions. Data collected with ZED 2 camera, the U-Net neural network model, and computer vision were used to reduce false positive predictions using a temporal window. Trigonometric calculations and camera parameters were used to estimate the vehicle’s position relative to the trajectory. One of the outcomes of this thesis is TuSimple dataset extension with the data captured with ZED 2 camera. Experimental verification demonstrated the system's functionality with high detection reliability in simple model situations, such as driving on a straight road segment. As the complexity of the model situations increased, the system's reliability decreases. Despite these shortcomings, the experiments showed that the system is able to detect lane boundaries and estimate an optimal vehicle trajectory. The algorithms for trajectory and vehicle position determination depend on the initial prediction of the lane boundaries, but they are functional and effective.
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Algorithm for Facial Image Quality Estimation
Husár, Tomáš ; Sakin, Martin (referee) ; Goldmann, Tomáš (advisor)
The precision of face recognition algorithms is heavily influenced by the quality of input images. The aim of the work is to evaluate the quality of face images using a convolutional neural network. The data on which the testing was carried out were created by various degradations of photos from the CelebA dataset. The resulting application determines the quality of images based on the predicted probabilities of individual degradations.
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System for tracking and classification of objects in the sky
Franka, Jakub ; Honec, Peter (referee) ; Janáková, Ilona (advisor)
This work deals with the use of computer vision in the field of detection, tracking and classification of flying objects in a real environment. The goal is to create a robust system, capable of working effectively in adverse conditions and accurately identifying different types of objects in the sky. The work progresses from theoretical foundations, choice of methods, to the design and implementation of a computer vision system.
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Object detection using ToF camera
Hlaváč, Martin ; Bastl, Petr (referee) ; Valach, Soběslav (advisor)
This thesis is about computer vision using a ToF camera. The thesis summarizes the history of ToF cameras and their use. The text also includes the parameters of the cameras I worked with and describes the used libraries, OpenCV and wxWidgets. The text also describes the proposal and subsequently the description of the created application intended for counting people. The work also includes a description of the functionality of the designed application and a proposal for possible further modifications of the application.
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Augmented Reality in Industrial Production and Maintenance
Kajan, Matej ; Janáková, Ilona (referee) ; Horák, Karel (advisor)
This paper seeks to explore the possibility to utilize XR (extended reality) in industrial assembly. The aim is to implement a system, which is able to visually navigate the operator during the product assembly process by the means of object recognition and image augmentation. The first chapter presents the use-case of augmented reality in the industry. The next part consists of research on the topic of augmented and virtual reality devices and provides a brief comparison of the current state of the art. Afterwards, a methodology is presented for object recognition of an arbitrary object. The implementation is able to detect the object in real-time, is resilient to occlusion and contains the information about the object’s orientation.
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Stereo reconstruction of vehicles' cross section
Boch, Jan ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
This master thesis deals with the design of a stereo system for processing vehicle images and subsequent reconstruction of their 3D model. The aim of the thesis is to reconstruct the vehicle model to control its dimensions and load on which restrictions are imposed in case of the use of critical transport infrastructure (tunnels, bridges). The thesis begins with a mathematical description of scene capture and continues with a research of theoretical possibilities of system configuration. One Intel RealSense 3D camera and one RGB camera were chosen as the most suitable configuration. The software solution works with the design of several different algorithms of which it is worth mentioning reconstruction using depth maps or triangulation.The resulting models in the last chapter contain data only from the aforementioned 3D camera. The model from the 3D camera could not be improved with the images from the RGB camera and camera can therefore be declared redundant. Resulting 3D model is still sufficient for the purpose of vehicle inspection and in case of continued work, another, more optimal configuration should be designed.
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Vehicle Make and Model Recognition
Gregor, Adam ; Špaňhel, Jakub (referee) ; Juránek, Roman (advisor)
V praktické části diplomové práce byla realizována úloha ozpoznání výrobce a modelu vozidla (VMMR). V první části byla pro účely strojového učení sestavena datová sada vozidel sestávající se z obrázků z Internetu. Takto bylo získáno přes 6 milionů obrázků aut, autobusů, motorek a dodávek, použitelných pro úlohu VMMR. Dále byla v rámci ex- perimentů na část datové sady použita standardní klasifikace, kdy na enkodér navazuje klasifikační vrstva realizovaná použitím neuronové sítě, a přístup, kdy za pomocí metody supervised contrastive learning byly embeddingy z enkodérů shlukovány za účelem snazší klasifikace. Jelikož první uvedený přístup vracel přesnější výsledky, byl použit v dalších experimentech. V nich se použilo větší množství obrázků z naší datové sady k natrénování klasifikátoru pro VMMR. Další klasifikátory byly natrénovány na datových sadách Stan- ford Cars a Comprehensive cars. Posléze bylo při porovnávání funkčnosti klasifikátorů na různých datových sadách shledáno, že klasifikátor trénovaný na naší datové sadě si vedl nejlépe.
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