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Tracking People in Video Captured from a Drone
Lukáč, Jakub ; Orság, Filip (oponent) ; Goldmann, Tomáš (vedoucí práce)
This thesis deals with the problem of determining the location of a person through their distance from camera approximation. The location is derived from video which is captured using a drone. The goal here is to propose and test existing solutions, and state-of-the-art algorithms for each encountered subproblem of the tracking. This means overcoming challenges such as object detection, re-identification of persons in time, estimating object distance from the camera and processing data from various sensors. Then, I am using these methods to design the final solution which can operate in nearly real-time. Implementation is based on the use of Intel NCS accelerator unit with the cooperation of small computer Raspberry Pi. Therefore, the setup may be easily mounted directly to a drone. The resulting application can generate tracking metadata for detected individuals in the recording. Afterwards, the positions are visualised as paths for better end-user presentation.
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Tracking People in Video Captured from a Drone
Lukáč, Jakub ; Orság, Filip (oponent) ; Goldmann, Tomáš (vedoucí práce)
This thesis deals with the problem of determining the location of a person and its approximation. The location is derived from video which is captured using a drone. The goal here is to propose and test existing solutions and state-of-the-art algorithms for each encountered subproblem. This means overcoming challenges such as object detection, re-identification of persons in time, estimating object distance from camera and processing data from various sensors. Then, I am using the methods to design the final solution which can operate in nearly real-time. Implementation is based on the use of Intel NCS accelerator unit with the cooperation of small computer Raspberry Pi. Therefore, the setup may be easily mounted directly to a drone. The resulting application can generate tracking metadata for detected individuals in the recording. Afterwards, the positions are visualised as paths for better end-user presentation.
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Neural Networks For Visual Classification And Inspection Of The Industrial Products
Míček, Vojtěch
The aim of this thesis is to enable evaluation of quality, or the type of product in industrial applications using artificial neural networks, especially in applications where the classical approach of machine vision is too complicated. The system thus designed is implemented onto a specific hardware platform and becomes a subject to the final optimalisation for the hardware platform for the best performance of the system.
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Tracking People in Video Captured from a Drone
Lukáč, Jakub ; Orság, Filip (oponent) ; Goldmann, Tomáš (vedoucí práce)
This thesis deals with the problem of determining the location of a person through their distance from camera approximation. The location is derived from video which is captured using a drone. The goal here is to propose and test existing solutions, and state-of-the-art algorithms for each encountered subproblem of the tracking. This means overcoming challenges such as object detection, re-identification of persons in time, estimating object distance from the camera and processing data from various sensors. Then, I am using these methods to design the final solution which can operate in nearly real-time. Implementation is based on the use of Intel NCS accelerator unit with the cooperation of small computer Raspberry Pi. Therefore, the setup may be easily mounted directly to a drone. The resulting application can generate tracking metadata for detected individuals in the recording. Afterwards, the positions are visualised as paths for better end-user presentation.
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Tracking People in Video Captured from a Drone
Lukáč, Jakub ; Orság, Filip (oponent) ; Goldmann, Tomáš (vedoucí práce)
This thesis deals with the problem of determining the location of a person and its approximation. The location is derived from video which is captured using a drone. The goal here is to propose and test existing solutions and state-of-the-art algorithms for each encountered subproblem. This means overcoming challenges such as object detection, re-identification of persons in time, estimating object distance from camera and processing data from various sensors. Then, I am using the methods to design the final solution which can operate in nearly real-time. Implementation is based on the use of Intel NCS accelerator unit with the cooperation of small computer Raspberry Pi. Therefore, the setup may be easily mounted directly to a drone. The resulting application can generate tracking metadata for detected individuals in the recording. Afterwards, the positions are visualised as paths for better end-user presentation.
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