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National Repository of Grey Literature 12 records found  previous11 - 12  jump to record: Search took 0.00 seconds. 
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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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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National Repository of Grey Literature : 12 records found   previous11 - 12  jump to record:
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