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Recognizing People and Their Activities in Video from Security Cameras
Saloň, Juraj Samuel ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to design and develop a system capable of recognizing the activities of people from surveillance cameras. Special attention is paid to the concept of complex situations or events that are defined by relations between identified objects. The first part surveys state-of-the-art techniques for object recognition, object tracking, and recognition of activities relevant to the realized solution. The second part describes the design and implementation of the devised system. It takes advantage of specific relations among two or more objects that are identified in video recordings, such as "person getting out of the car" or "one or more people met with a person of interest and they left together". Results are evaluated on video data extracted from available datasets and manually annotated. The mean average precision metric (MAP) on the data is reported.
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An automatic football match event detection
Dvonč, Tomáš ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This diploma thesis describes methods suitable for automatic detection of events from video sequences focused on football matches. The first part of the work is focused on the analysis and creation of procedures for extracting informations from available data. The second part deals with the implementation of selected methods and neural network algorithm for corner kick detection. Two experiments were performed in this work. The first captures static information from one image and the second is focused on detection from spatio-temporal data. The output of this work is a program for automatic event detection, which can be used to interpret the results of the experiments. This work may figure as a basis to gain new knowledge about the issue and also to the further development of detection events from football.
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Understanding of Badminton Videos
Mašláň, Vojtěch ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
The aim of this thesis was to experiment with machine learning models for the understanding of badminton videos. The thesis maps the current state of using computer vision and machine learning for sport videos analysis. In the experimental part of the work, several models for badminton stroke detection were made. All the models are predicting the strokes based on player poses extracted from a pose estimation model. Developed models achieved an accuracy of 80.1 % for detecting 7 different strokes and 84.0 % for detecting 4 different strokes. Trained models were then used to create a simple web application for short badminton video analysis.
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Recognizing People and Their Activities in Video from Security Cameras
Saloň, Juraj Samuel ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to design and develop a system capable of recognizing the activities of people from surveillance cameras. Special attention is paid to the concept of complex situations or events that are defined by relations between identified objects. The first part surveys state-of-the-art techniques for object recognition, object tracking, and recognition of activities relevant to the realized solution. The second part describes the design and implementation of the devised system. It takes advantage of specific relations among two or more objects that are identified in video recordings, such as "person getting out of the car" or "one or more people met with a person of interest and they left together". Results are evaluated on video data extracted from available datasets and manually annotated. The mean average precision metric (MAP) on the data is reported.
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An automatic football match event detection
Dvonč, Tomáš ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This diploma thesis describes methods suitable for automatic detection of events from video sequences focused on football matches. The first part of the work is focused on the analysis and creation of procedures for extracting informations from available data. The second part deals with the implementation of selected methods and neural network algorithm for corner kick detection. Two experiments were performed in this work. The first captures static information from one image and the second is focused on detection from spatio-temporal data. The output of this work is a program for automatic event detection, which can be used to interpret the results of the experiments. This work may figure as a basis to gain new knowledge about the issue and also to the further development of detection events from football.
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