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Pedestrian Detection and Recognition in a Multi-Camera System
Macák, Filip ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The main purpose of this bachelor's thesis is to create an application for person detection and recognition from scenes captured in a multi-camera system. The output of the application is a video on which the detected persons are highlighted and each person is assigned an identification number through which it can be recognized across the input scenes. Several solutions to the problem of person detection and recognition were examined and the text of this work serves as an overview of these problems. The application is built on PyTorch and Torchreid libraries. A detector with a Faster-RCNN network is used for detection and recognition is based on the OSNet network. The application also includes a simple user interface to facilitate work with the application. The application serves as a demonstration of the state-of-the-art for person detection and recognition.
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Tracking of Moving Objects in Video
Kroupa, Dominik ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
This thesis deals with the issue of multi-target multi-camera tracking, which consists of detection, tracking and re-identification of objects. The framework designed to deal with this problem uses state-of-the art methods followed by post-processing of data retrieved by these methods to improve overall accuracy and object re-identification across cameras. The data obtained by the multi-target multi-camera tracking can be further used in tasks such as crowd behaviour analysis in case of pedestrian tracking, or traffic management in case of vehicle tracking. As part of this solution, this work took part in the new challenge of pedestrian tracking, issued by AI City Challenge for year 2023, with a score of 0.2533 IDF1, resulting in 21st place.
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Detection and Recognition of Persons in a Multi-Camera System
Martinček, Ľuboš ; Beran, Jan (referee) ; Goldmann, Tomáš (advisor)
This thesis deals with the detection and recognition of people in a multi-camera system. In this thesis I describe general camera systems and their development and methods used for detection and recognition of persons. Based on this information, in the second part of the project I describe the design and implementation of a multi-camera system that is able to detect and recognize people. This thesis implements a combination of a YOLO detector and an Omni-Scale neural network based feature extractor.
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Pedestrian Detection and Recognition in a Multi-Camera System
Macák, Filip ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The main purpose of this bachelor's thesis is to create an application for person detection and recognition from scenes captured in a multi-camera system. The output of the application is a video on which the detected persons are highlighted and each person is assigned an identification number through which it can be recognized across the input scenes. Several solutions to the problem of person detection and recognition were examined and the text of this work serves as an overview of these problems. The application is built on PyTorch and Torchreid libraries. A detector with a Faster-RCNN network is used for detection and recognition is based on the OSNet network. The application also includes a simple user interface to facilitate work with the application. The application serves as a demonstration of the state-of-the-art for person detection and recognition.
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