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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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Tracking of Moving Objects in Video
Folenta, Ján ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This bachelor thesis deals with the issue of detection, tracking and counting vehicles in different directions in video. To deal with this problem, modern techniques of object detection and tracking using convolutional neural networks are used. The goal of this work is to achieve highest possible accuracy of vehicle counting while maintaining the processing of video recordings in real-time. The problems of the implemented method for detection and tracking are solved by analyzing and working with the trajectories of vehicles. With accuracy of 90,94% and total score of 0,8829, this work participated in AI City Challenge 2020, where it placed 6th.
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Detection of objects and tracking the route of movement of traffic participants for the needs of intelligent transport nodes
Vymazal, Tomáš ; Kiac, Martin (referee) ; Burget, Radim (advisor)
The master‘s thesis is focused on the object detection. The aim of this thesis is to desine an experiment to assess the detection models YOLOv5, YOLOR, Scaled-YOLOv4 and EfficientDet and to compare their properties (detection speed, memory requirements, accuracy and certainty of detection). For this purpose a custom data set is created to investigate these parameters. The study shows that the YOLOv5 network is performd as the best solution. Deep SORT is used for object tracking which is important for the subsequent extraction of training data from video footage for object movement prediction. The added value is the design of the prediction algorithm which is based on a polynomial regression model.
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Tracking of Moving Objects in Video
Folenta, Ján ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This bachelor thesis deals with the issue of detection, tracking and counting vehicles in different directions in video. To deal with this problem, modern techniques of object detection and tracking using convolutional neural networks are used. The goal of this work is to achieve highest possible accuracy of vehicle counting while maintaining the processing of video recordings in real-time. The problems of the implemented method for detection and tracking are solved by analyzing and working with the trajectories of vehicles. With accuracy of 90,94% and total score of 0,8829, this work participated in AI City Challenge 2020, where it placed 6th.
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Visual tracking systém pro UAV
KOLÁŘ, Michal
This master thesis deals with the analysis of the current possibilities for object tracking in the image, based on which is designed a procedure for creating a system capable of tracking an object of interest. Part of this work is designing virtual reality for the needs of implementation of the tracking system, which is finally deployed and tested on a real prototype of unmanned vehicle.
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