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Traffic Monitoring from Aerial Video Data
Babinec, Adam ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis proposes a system for extraction of vehicle trajectories from aerial video data for traffic analysis. The system is designed to analyse video sequence of a single traffic scene captured by an action camera mounted on an arbitrary UAV flying at the altitudes of approximately 150 m. Each video frame is geo-registered using visual correspondence of extracted ORB features. For the detection of vehicles, MB-LBP classifier cascade is deployed, with additional step of pre-filtering of detection candidates based on movement and scene context. Multi-object tracking is achieved by Bayesian bootstrap filter with an aid of the detection algorithm. The performance of the system was evaluated on three extensively annotated datasets. The results show that on the average, 92% of all extracted trajectories are corresponding to the reality. The system is already being used in the research to aid the process of design and analysis of road infrastructures.
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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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Modelling eye movements during Multiple Object Tracking
Děchtěrenko, Filip ; Lukavský, Jiří (advisor) ; Toth, Peter Gabriel (referee)
In everyday situations people have to track several objects at once (e.g. driving or collective sports). Multiple object tracking paradigm (MOT) plausibly simulate tracking several targets in laboratory conditions. When we track targets in tasks with many other objects in scene, it becomes difficult to discriminate objects in periphery (crowding). Although tracking could be done only using attention, it is interesting question how humans plan their eye movements during tracking. In our study, we conducted a MOT experiment in which we presented participants repeatedly several trials with varied number of distractors, we recorded eye movements and we measured consistency of eye movements using Normalized scanpath saliency (NSS) metric. We created several analytical strategies employing crowding avoidance and compared them with eye data. Beside analytical models, we trained neural networks to predict eye movements in MOT trial. The performance of the proposed models and neuron networks was evaluated in a new MOT experiment. The analytical models explained variability of eye movements well (results comparable to intraindividual noise in the data); predictions based on neural networks were less successful.
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Comparison of scan patterns in dynamic tasks
Děchtěrenko, Filip ; Lukavský, Jiří (advisor) ; Nyström, Marcus (referee) ; Paluš, Milan (referee)
Eye tracking is commonly used in many scientific fields (experimental psychology, neuroscience, behavioral economics, etc.) and can provide us with rigorous data about current allocation of attention. Due to the complexity of data processing and missing methodology, experimental designs are often limited to static stimuli; eye tracking data is analyzed only with respect to basic types of eye movements - fixation and saccades. In dynamic tasks (e.g. with dynamic stimuli, such as showing movies or Multiple Object Tracking task), another type of eye movement is commonly present: smooth pursuit. Importantly, eye tracking data from dynamic tasks is often represented as raw data samples. It requires a different approach to analyze the data, and there are a lot of methodological gaps in analytical tools. This thesis is divided into three parts. In the first part, we gave an overview of current methods for analyzing scan patterns, followed by four simulations, in which we systematically distort scan patterns and measure the similarity using several commonly used metrics. In the second part, we presented the current approaches to statistical testing of differences between groups of scan patterns. We present two novel strategies for analyzing statistically significant differences between groups of scan patterns and...
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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the...
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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the similarity between...
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Comparison of scan patterns in dynamic tasks
Děchtěrenko, Filip ; Lukavský, Jiří (advisor) ; Nyström, Marcus (referee) ; Paluš, Milan (referee)
Eye tracking is commonly used in many scientific fields (experimental psychology, neuroscience, behavioral economics, etc.) and can provide us with rigorous data about current allocation of attention. Due to the complexity of data processing and missing methodology, experimental designs are often limited to static stimuli; eye tracking data is analyzed only with respect to basic types of eye movements - fixation and saccades. In dynamic tasks (e.g. with dynamic stimuli, such as showing movies or Multiple Object Tracking task), another type of eye movement is commonly present: smooth pursuit. Importantly, eye tracking data from dynamic tasks is often represented as raw data samples. It requires a different approach to analyze the data, and there are a lot of methodological gaps in analytical tools. This thesis is divided into three parts. In the first part, we gave an overview of current methods for analyzing scan patterns, followed by four simulations, in which we systematically distort scan patterns and measure the similarity using several commonly used metrics. In the second part, we presented the current approaches to statistical testing of differences between groups of scan patterns. We present two novel strategies for analyzing statistically significant differences between groups of scan patterns and...
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Predicting targets in Multiple Object Tracking task
Citorík, Juraj ; Děchtěrenko, Filip (advisor) ; Brunetto, Robert (referee)
The aim of this thesis is to predict targets in a Multiple Object Tracking (MOT) task, in which subjects track multiple moving objects. We processed and analyzed data containing object and gaze position information from 1148 MOT trials completed by 20 subjects. We extracted multiple features from the raw data and designed a machine learning approach for the prediction of targets using neural networks and hidden Markov models. We assessed the performance of the models and features. The results of our experiments show that it is possible to train a machine learning model to predict targets with very high accuracy. 1
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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the...
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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the similarity between...
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