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Automatic People Counting from Panoramic Photography
Blucha, Ondřej ; Kolář, Martin (referee) ; Veľas, Martin (advisor)
This bachelor thesis deals with automatic people counting from panoramic photography. This is very useful for counting large number of people, such as on the stadium or on the concerts. It consists of the two parts. The first one is image stitching, which process the images by the feature-based methods. The second part is people counting using face detection, where were used Viola-Jones detector. The ideal setting of parameters for used methods was experimentally selected.
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Visual Car-Detection on the Parking Lots Using Deep Neural Networks
Stránský, Václav ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
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Cloud Solution for 3D Models Processing
Klemens, Jakub ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis deals with the possibilities of processing 3D models in cloud applications. A C++ library called Cloud3D has been designed and implemented. The resulting library is used to quickly create client-server applications. The library is divided into three separate parts: Client, Service Provider and Load Balancer. The service provider runs in the cloud and provides 3D model processing services to client applications. The biggest advantage of Cloud3D is the ease of creating new applications with its help. Other benefits include scalability, assured implementation of look-a-side Load Balancer, and security ensured by the use of SSL certification.
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Road Detection for Autonomous Car
Komora, Matúš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
his thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.
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Object Detection in the Laser Scans Using Convolutional Neural Networks
Zelenák, Michal ; Kodym, Oldřich (referee) ; Veľas, Martin (advisor)
This work is focused on road segmentation in laser scans, using a convolutional neural network. To achieve this goal, which will find application in the field of road maintenance, convolutional neural networks have been used for their flexibility and speed. The work brings implementation and modifications of the existing method, which solves the problem by using a fully connected convolutional neural network. Used modifications include, for example using of various parameters for the loss function, the use of a different number of classes in the network model and dataset. The effect of the modification was experimentally verified and the accuracy of 96.12%, and the value for F-measure 95.02% were achieved.
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Object Detection Using Kinect
Němec, Lukáš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This paper address the problem of object recognition using Microsoft Kinect in the fi eld of computer vision. The objective of this work was to evaluate current methods of detection of objects using depth map (RGB-D sensor). The work deals with the enviroment of point cloud and Viewpoint Feature method. It also describes the use of binary classifi er in the context of object recognition. Object detection was implemented and performed experiments with it.
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Navigation Using Deep Convolutional Networks
Skácel, Dalibor ; Veľas, Martin (referee) ; Hradiš, Michal (advisor)
This thesis studies navigation and autonomous driving using convolutional neural networks. It presents main approaches to this problem used in literature. It describes theory of neural networks and imitation and reinforcement learning. It also describes tools and methods suitable for a driving system. There are two simulation driving models created using learning algorithms DAGGER and DDPG. The models are then tested in car racing simulator TORCS.
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I Offer/Seek Local Help. How to Connect These People Effectively and Safely?
Kohútová, Alena ; Veľas, Martin (referee) ; Beran, Vítězslav (advisor)
The aim of this bachelor thesis is to design a system that will connect people seeking help with those who need it, focusing on the effectiveness of use and motivation of users using gamification techniques. The system allows you to add contribution, start user collaboration, add ratings to other users and more. The resulting web system is implemented using modern web design techniques. The output of the work is an implemented prototype, which is evaluated by user testing.
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3D Objects Detection in Images
Bordovský, Gabriel ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This bachelors thesis deals with detection of a known 3D object in images and its pose estimation. The method uses the ORB-type keypoints and their location on the surface of a bounding box. By using solve of PnP problem a pose of the object is obtained using the points 2D coordinates from the image and the 3D coordinates of the very points from the registered model. This thesis expands a detection method for simple box-shaped objects, which is a part of OpenCV library, for the usage on more complex objects. In experiments, the detector reached a detection success rate of 85 % and the computed pose matches the real one approximately for 88 %.
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Object Detection in the Laser Scans Using Convolutional Neural Networks
Marko, Peter ; Beran, Vítězslav (referee) ; Veľas, Martin (advisor)
This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-information system. During testing, the U-Net achieved a segmentation accuracy of 98.8\%, a specificity of 99.5\% and a sensitivity of 72.9\%. The estimated polyline reached an average deviation of 1.8cm.
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