National Repository of Grey Literature 4 records found  Search took 0.01 seconds. 
Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
Computer vision for mechatronic applications using the OpenCV library
Černil, Martin ; Spáčil, Tomáš (referee) ; Bastl, Michal (advisor)
This thesis introduces a computer vision library OpenCV, which is subsequently implemented into three distinguishably different problems using Python programming language. These three problems are identifying an object and its location, object tracking and difference detection, safe distance qualification using a depth map.
Computer vision for mechatronic applications using the OpenCV library
Černil, Martin ; Spáčil, Tomáš (referee) ; Bastl, Michal (advisor)
This thesis introduces a computer vision library OpenCV, which is subsequently implemented into three distinguishably different problems using Python programming language. These three problems are identifying an object and its location, object tracking and difference detection, safe distance qualification using a depth map.
Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.

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