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Person's identification by means of bipedal locomotion
Krzyžanek, Jakub ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The aim of this thesis is to recognize a walking person in a sequence of images by defining his or her reference points to compare the course of their movement and then to identify the scanned person. Methods „k-means“ and „mean shift“ are used to obtain the silhouette of the person. However “environment model estimation” method is used here before those mentioned above. It is a type of a difference method and it helps to specify the scanning area and shortens the time of segmentation. During the search for the reference points the thesis focuses on three areas: the centre of the head and both ankle joints. Those points are later determined on the previous image sequence and compared with the real locations of the centre of the head and ankle joints marked by the user. The thesis also focuses on comparing the movement courses of those points and tries to identify the people whose walks are being scanned. Problematic situations which occurred during the whole process are analyzed in the end. The result of the thesis is an algorithm which can locate a moving person in an image sequence (video) and determine the reference points (centre of the head and ankles) to compare them and identify the scanned person.
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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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Pedestrian Identification
Jurča, Jan ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This thesis deals with pedestrian identification from video sequence based on person, face and gait recognition. For person and face recognition are used pretrained networks. While for gait recognition is implemented and compared many different networks. Final pedestrian recognition is based on multimodal fusion realized by neural network. For the purpose of the work was created dataset, along with a set of tools that allow its almost automatic creation.
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Pedestrian Identification
Jurča, Jan ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This thesis deals with pedestrian identification from video sequence based on person, face and gait recognition. For person and face recognition are used pretrained networks. While for gait recognition is implemented and compared many different networks. Final pedestrian recognition is based on multimodal fusion realized by neural network. For the purpose of the work was created dataset, along with a set of tools that allow its almost automatic creation.
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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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|
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Person's identification by means of bipedal locomotion
Krzyžanek, Jakub ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The aim of this thesis is to recognize a walking person in a sequence of images by defining his or her reference points to compare the course of their movement and then to identify the scanned person. Methods „k-means“ and „mean shift“ are used to obtain the silhouette of the person. However “environment model estimation” method is used here before those mentioned above. It is a type of a difference method and it helps to specify the scanning area and shortens the time of segmentation. During the search for the reference points the thesis focuses on three areas: the centre of the head and both ankle joints. Those points are later determined on the previous image sequence and compared with the real locations of the centre of the head and ankle joints marked by the user. The thesis also focuses on comparing the movement courses of those points and tries to identify the people whose walks are being scanned. Problematic situations which occurred during the whole process are analyzed in the end. The result of the thesis is an algorithm which can locate a moving person in an image sequence (video) and determine the reference points (centre of the head and ankles) to compare them and identify the scanned person.
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