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Preemptive Safety Analysis of Road Users' Behavior from Trajectories
Zapletal, Dominik ; Herout, Adam (referee) ; Zendulka, Jaroslav (advisor)
This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.
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Re-Identification of Vehicles in Video
Zapletal, Dominik ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This thesis deals with the vehicle re-identification in video problem. Vehicle re-identification is based on matching image parts obtained from different cameras. This work is focues on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms, histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the Full HD resolution video input. The applications of this work include finding important parameters like travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.
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Preemptive Safety Analysis of Road Users' Behavior from Trajectories
Zapletal, Dominik ; Herout, Adam (referee) ; Zendulka, Jaroslav (advisor)
This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.
|
|
|
Re-Identification of Vehicles in Video
Zapletal, Dominik ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This thesis deals with the vehicle re-identification in video problem. Vehicle re-identification is based on matching image parts obtained from different cameras. This work is focues on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms, histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the Full HD resolution video input. The applications of this work include finding important parameters like travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.
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