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Multimodal System for Multi-Object Tracking in Real-Time
Kučera, Adam ; Šátek, Václav (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with the topic of multi-object multi-sensor tracking. A conventional track-oriented multiple hypothesis tracking (TOMHT) pipeline is implemented in C++ programming language and an implementable interface is designed, enabling to easily extend the core algorithm with arbitrary sensors and measured target attributes, making the system multimodal, i.e.\ applicable in heterogeneous systems of sensors. A novel algorithm for solving combinatorial optimization arising in TOMHT is proposed. Finally, few example implementations of the interface are provided and the system is evaluated in simulated and real-world scenarios.
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Multimodal System for Multi-Object Tracking in Real-Time
Kučera, Adam ; Šátek, Václav (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with the topic of multi-object multi-sensor tracking. A conventional track-oriented multiple hypothesis tracking (TOMHT) pipeline is implemented in C++ programming language and an implementable interface is designed, enabling to easily extend the core algorithm with arbitrary sensors and measured target attributes, making the system multimodal, i.e.\ applicable in heterogeneous systems of sensors. A novel algorithm for solving combinatorial optimization arising in TOMHT is proposed. Finally, few example implementations of the interface are provided and the system is evaluated in simulated and real-world scenarios.
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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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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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