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3D Reconstruction of Human Body Motion (MOCAP)
Černák, František ; Svoboda, Pavel (referee) ; Šolony, Marek (advisor)
This thesis deals with object recognition in image, motion capturing and his transformation into 3D coordination system and 3D reconstruction. The problem was solved by building stereo camera system, detection of markers on a moving object and getting his position in space using triangulation. Hit rate of 95% was achieved in marker detection at close range and accuracy of 3D reconstruction answers to reality at certain extend. The main finding of this work is that it is possible to reconstruct body motions with a pair of simple web cameras.
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Acquisition of inputs by image processing for controlling an autonomous vehicle
Midrla, Daniel ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This master’s thesis deals with data acquisition by image processing in order to control an autonomous vehicle. Firstly, the thesis offers a summary of theoretical knowledge relevant to the given topic. Then follows a description of creating an algorithm, which acquires basic inputs for autonomous vehicle control with the use of a stereo camera and an object detection neural network. The inputs gained from this algorithm are the class of the detected object and its distance. Finally, an experimental evaluation of the correct functionality is performed with an emphasis on optimizing the accuracy and range of the distance computation. An assessment of the ability to deploy the created algorithm in real time on a compact computer with limited computing power is also performed.
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LIDAR and Stereocamera in Localization of Mobile Robots
Vyroubalová, Jana ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
LIDAR (2D) has been widely used for mapping, localization and navigation in mobile robotics. However, its usage is limited to simple environments. This problem can be solved by adding more sensors and processing these data together. This paper explores a method how measurements from a stereo camera and LIDAR are fused to dynamical mapping. An occupancy grid map from LIDAR data is used as prerequisite and extended by a 2D grid map from stereo camera. This approach is based on the ground plane estimation in disparity map acquired from the stereo vision. For the ground plane detection, RANSAC and Least Squares methods are used. After obstacles determination, 2D occupancy map is generated. The output of this method is 2D map as a fusion of complementary maps from LIDAR and camera. Experimental results obtained from RUDA robot and MIT Stata Center Data Set are good enough to determine that this method is a benefit, although my implementation is still a prototype. In this paper, we present the applied methods, analyze the results and discuss the modifications and possible extensions to get better results.
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Evaluation of Methods of Stereo Image Processing
Juráček, Ivo ; Španěl, Michal (referee) ; Zemčík, Pavel (advisor)
This thesis addresses the correlation between two pictures and its application to a real example using a camera which is a part of a stereo camera. It suggests how to use correlation to find specific patterns in pictures whereas it is possible to calculate the movement between two scaled pictures which is done by resampling. The accuracy of correlation is then shown when pictures are compensated using affine transformation in order to verify the accuracy of correlation. To further increase the correlation accuracy, it is possible to use a corner detector, it finds main points in a shot and then it searches for a match in another shot using correlation. Final results using this method are also mentioned in this thesis.
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Acquisition of inputs by image processing for controlling an autonomous vehicle
Midrla, Daniel ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This master’s thesis deals with data acquisition by image processing in order to control an autonomous vehicle. Firstly, the thesis offers a summary of theoretical knowledge relevant to the given topic. Then follows a description of creating an algorithm, which acquires basic inputs for autonomous vehicle control with the use of a stereo camera and an object detection neural network. The inputs gained from this algorithm are the class of the detected object and its distance. Finally, an experimental evaluation of the correct functionality is performed with an emphasis on optimizing the accuracy and range of the distance computation. An assessment of the ability to deploy the created algorithm in real time on a compact computer with limited computing power is also performed.
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|
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Evaluation of Methods of Stereo Image Processing
Juráček, Ivo ; Španěl, Michal (referee) ; Zemčík, Pavel (advisor)
This thesis addresses the correlation between two pictures and its application to a real example using a camera which is a part of a stereo camera. It suggests how to use correlation to find specific patterns in pictures whereas it is possible to calculate the movement between two scaled pictures which is done by resampling. The accuracy of correlation is then shown when pictures are compensated using affine transformation in order to verify the accuracy of correlation. To further increase the correlation accuracy, it is possible to use a corner detector, it finds main points in a shot and then it searches for a match in another shot using correlation. Final results using this method are also mentioned in this thesis.
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LIDAR and Stereocamera in Localization of Mobile Robots
Vyroubalová, Jana ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
LIDAR (2D) has been widely used for mapping, localization and navigation in mobile robotics. However, its usage is limited to simple environments. This problem can be solved by adding more sensors and processing these data together. This paper explores a method how measurements from a stereo camera and LIDAR are fused to dynamical mapping. An occupancy grid map from LIDAR data is used as prerequisite and extended by a 2D grid map from stereo camera. This approach is based on the ground plane estimation in disparity map acquired from the stereo vision. For the ground plane detection, RANSAC and Least Squares methods are used. After obstacles determination, 2D occupancy map is generated. The output of this method is 2D map as a fusion of complementary maps from LIDAR and camera. Experimental results obtained from RUDA robot and MIT Stata Center Data Set are good enough to determine that this method is a benefit, although my implementation is still a prototype. In this paper, we present the applied methods, analyze the results and discuss the modifications and possible extensions to get better results.
|
|
|
3D Reconstruction of Human Body Motion (MOCAP)
Černák, František ; Svoboda, Pavel (referee) ; Šolony, Marek (advisor)
This thesis deals with object recognition in image, motion capturing and his transformation into 3D coordination system and 3D reconstruction. The problem was solved by building stereo camera system, detection of markers on a moving object and getting his position in space using triangulation. Hit rate of 95% was achieved in marker detection at close range and accuracy of 3D reconstruction answers to reality at certain extend. The main finding of this work is that it is possible to reconstruct body motions with a pair of simple web cameras.
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