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Person Identification Based on Locomotion
Pražák, Ondřej ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This paper deals with study of human movement and using that in identification. In the first part of my work are explained characteristics of human movement and factors which take effect on these characteristics. Practical part is dealing with design of program which is solving mentioned problems. The input of program is created by video sequence with lateral movement of human. The program is finding coordinates of lower limbs joints. From this coordinates are created locomotion characteristics used for human identification. Matching of time behaviors is based on correlation.
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Using the Kinect sensor in detection of people
Janás, Lukáš ; Šmirg, Ondřej (referee) ; Přinosil, Jiří (advisor)
Bachelor thesis deals with the methods of people detection by using Microsoft Kinect. Depth image captured by this device is further processed by using standard algorithms from freely available OpenCV library. All of image processing methods are described in detail of their function and influence to image. The practical part of the work is focused on the realization of a simple program, which is serving for image segmentation and finding people.
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Perimeter Monitoring and Intrusion Detection Based on Camera Surveillance
Goldmann, Tomáš ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This bachelor thesis contains a description of the basic system for perimeter monitoring. The main part of the thesis introduces the methods of computer vision suitable for detection and classification of objects. Furthermore, I devised an algorithm based on background subtraction which uses a Histogram of Oriented Gradients for description of objects and an SVM classifier for their classification. The final part of the thesis consists of a comparison of the descriptor based on the Histogram of Oriented Gradients and the SIFT descriptor and an evaluation of precision of the detection algorithm.
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Human Presence Detection and Applications in Smart Home Environments
Dostál, Pavel ; Juránek, Roman (referee) ; Černocký, Jan (advisor)
Práce se zabývá studiem detekce a rozpoznávání člověka a následnou implementací v prostředí softwaru Home Manager, což je projekt simulující chytrý dům s intelligentními zařízeními. Rozšíření o funkcionalitu rozpoznání uživatelů a narušitelů umožní lépe přizpůsobit chování zařízení preferencím jednotlivých osob. Použití rozpoznávání je demonstrováno na ukázce ovládání klimatizace v místnostech podle přítomnosti uživatelů.
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Human Detection Using Radar
Skácel, Jan ; Zemčík, Pavel (referee) ; Maršík, Lukáš (advisor)
This work is focused on the design and implementation of algorithm for human detection in real time using a continuous wave radar. Classification of people is based on doppler signatures produced when they walk. Fourier transform techniques were used to analyze these signatures and key features were identified that are very representative of the human walking motion. On the basis of design, the C++ application that process radar signal and detects human is implemented. Last part of this work is focused on algorithm evaluation.
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Detection of Anomalies in Pedestrian Walking
Pokorný, Ondřej ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The goal of this work was to create a system that would be able to detect anomalies in pedestrian walking. As the core of my application, I have used OpenPose, which is an application for detecting human skeletons. Then I used a bidirectional LSTM neural network to detect anomalies in video sequences. This architecture was chosen during the experiment because it outperformed other solutions. I trained my model to detect three types of anomalies. The output of my application is a video with marked sequences of anomalies. The whole system is implemented in Python.
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Estimate the Height of a Persons from the Video Data
Jelen, Vilém ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The aim of this bachelor thesis is to create an application that can detect pedestrians and make an estimate of their height from the video data. In the main part of the thesis I introduced security cameras and image processing methods suitable for detecting and classifying objects. Based on the comparison of these methods, I realized the pedestrian detection using Histograms of Oriented Gradients. As the input parameters of the proposed height estimation algorithm, I used the position and properties of the camera and reference objects or points.
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Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
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Detection of Anomalies in Pedestrian Walking
Pokorný, Ondřej ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The goal of this work was to create a system that would be able to detect anomalies in pedestrian walking. As the core of my application, I have used OpenPose, which is an application for detecting human skeletons. Then I used a bidirectional LSTM neural network to detect anomalies in video sequences. This architecture was chosen during the experiment because it outperformed other solutions. I trained my model to detect three types of anomalies. The output of my application is a video with marked sequences of anomalies. The whole system is implemented in Python.
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Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
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