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Ball Tracking in Sports Video
Motlík, Matúš ; Špaňhel, Jakub (referee) ; Bartl, Vojtěch (advisor)
This master's thesis deals with automatic detection and tracking of a soccer ball in sports videos. Based on the introduced techniques focusing on tracking of small objects in high-resolution videos, effective convolutional neural networks are designed and used by a modified version of tracking algorithm SORT for automatic object detection. A set of experiments with the processing of images in different resolutions and with various frequencies of detection extraction is carried out in order to examine the trade-off between processing speed and tracking accuracy. The obtained results of experiments are presented and used to form proposals for future work, which could lead to improvements in tracking accuracy while maintaining reasonable processing speed.
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Analysis of Surveillance Camera Recordings
Ščavnická, Šárka ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
This thesis deals with the systems for analyzing records from security cameras. It aims to create a functional solution that analyzes records and answers questions from the user. The created system combines the YOLO algorithm for object detection and DeepSORT for their subsequent tracking. It contains five models that detect specific situations. Individual models achieved varying degrees of success during testing, with the lowest success rate being 58 % for the getting out of car situation. The highest success rate, 83 %, was obtained by a model for detecting a meeting between two people.
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Multi-Person Tracking in Video from Mono-Camera
Vojvoda, Jakub ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
Multiple person detection and tracking is challenging problem with high application potential. The difficulty of the problem is caused mainly by complexity of scene and large variations in articulation and appearance of person. The aim of this work is to design and implement system capable of detecting and tracking people in video from static mono-camera. For this purpose, an online method for tracking has been proposed based on tracking-by-detection approach. The method combines detection, tracking and fusion of responses to achieve accurate results. The implementation was evaluated on available dataset and the results show that it is suitable to use for this task. A method for motion segmentation was proposed and implemented to improve the tracking results. Furthermore, implementation of detector based on histogram of oriented gradients was accelerated by taking advantage of graphics processing unit (GPU).
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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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Video Anonymization
Mokrý, Martin ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The goal of this thesis is to design and create an automatic system for video anonymization. This system makes use of various object detectors on an image to ensure functionality, as well as active tracking of objects detected in this manner. Adjustments are later applied to these detected objects which ensure sufficient level of anonymization. The main asset of this system is speeding up the anonymization process of videos that can be published after.
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Predicting Trajectories of Vehicles and Pedestrians for Driving Assistent Systems
Mudroň, Marek ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
This bachelor thesis deals with representation of a traffic scene by processing monocular video sequence. I try to predict a trajectory of detected vehicles in a short time horizon, based on created representation. Current approaches use multiple expensive sensors to gather instant information of environment. In the thesis I introduce technique, which is able to extract data from an environment by image processing techniques without the need of expensive sensors. The result of this work is a system creating opportunity to reduce the sensor costs of a system for scene representation and trajectory prediction of vehicles in the scene. In addition, comparison of models trained on differently processed data is provided, as well as data about how my system approximates the most reliable prediction models.
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Artificial Intelligence for Video Sonification
Dobrocký, Filip ; Burget, Radim (referee) ; Říha, Kamil (advisor)
This thesis deals with the topic of video sonification – the transformation of image into sound. It aims to use state-of-the-art techniques of computer vision based on artificial intelligence to create a system capable of algorithmic sound creation applicable in the art context. The focus is put on the fields of sound art, algorithmic composition and generative music. The thesis includes an implementation of a modular sonification system which utilizes the modern object detector YOLOv7 along with a multiple object tracking algorithm (implemented in the library Norfair), built using the programming language Python. The fundementals of the system lie in systematic assignment of sound objects to objects tracked in the video. The sound creation relies on the SuperCollider platform using the Python API Supriya, incorporating various methods of sound synthesis along with a programmatically created sound database.
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Predicting Trajectories of Vehicles and Pedestrians for Driving Assistent Systems
Mudroň, Marek ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
This bachelor thesis deals with representation of a traffic scene by processing monocular video sequence. I try to predict a trajectory of detected vehicles in a short time horizon, based on created representation. Current approaches use multiple expensive sensors to gather instant information of environment. In the thesis I introduce technique, which is able to extract data from an environment by image processing techniques without the need of expensive sensors. The result of this work is a system creating opportunity to reduce the sensor costs of a system for scene representation and trajectory prediction of vehicles in the scene. In addition, comparison of models trained on differently processed data is provided, as well as data about how my system approximates the most reliable prediction models.
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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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Analysis of Surveillance Camera Recordings
Ščavnická, Šárka ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
This thesis deals with the systems for analyzing records from security cameras. It aims to create a functional solution that analyzes records and answers questions from the user. The created system combines the YOLO algorithm for object detection and DeepSORT for their subsequent tracking. It contains five models that detect specific situations. Individual models achieved varying degrees of success during testing, with the lowest success rate being 58 % for the getting out of car situation. The highest success rate, 83 %, was obtained by a model for detecting a meeting between two people.
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