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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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Basics of Pedestrians Detection in Image by Machine Learning
Lučanský, Peter ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Táto Bakalárska práce sa zaoberá významnou problematikou v oblasti počítačového videnia, ktorou je detekcia osôb/chodcov v obraze, za pomoci metod strojového učenia, spolu s jej možným využitím, vývojom a vysvetlením princípov. Taktiež sa zaoberá testovaním dnes najlepšieho dostupného algoritmu, pričom sa porovnávajú faktory ktoré vplívajú na kvalitu jeho činnosti. Na začiatku je problematika stručne popísaná, potom sa prejde k podrobným popisom dosiahnutých pokrokov. V nasledujúcej časti sú popísané dostupné datasety, ktoré by sa dali použiť pri tréningu detekčného algoritmu. V poslednom rade sú vykonané trénovacie procesy za rozličných podmienok, pričom sú jednotlivé výsledky porovnávané.
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Image object detector based on C4 algorithm
Vylíčil, Radek ; Rajmic, Pavel (referee) ; Mašek, Jan (advisor)
This thesis deals with the image object detection using Contour cues. The text describes methods how to train and test object detector. The main contribution of this thesis consists in creation Feature extractor for creation object detector in Java programming. The functionality of object detector was demonstrated on medical images.
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Efficiency of deep convolutional neural networks on an elementary classification task
Prax, Jan ; Dobrovský, Ladislav (referee) ; Škrabánek, Pavel (advisor)
In this thesis deep convolutional neural networks models and feature descriptor models are compared. Feature descriptors are paired with suitable chosen classifier. These models are a part of machine learning therefore machine learning types are described in this thesis. Further these chosen models are described, and their basics and problems are explained. Hardware and software used for tests is listed and then test results and results summary is listed. Then comparison based on the validation accuracy and training time of these said models is done.
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Efficiency of deep convolutional neural networks on an elementary classification task
Prax, Jan ; Dobrovský, Ladislav (referee) ; Škrabánek, Pavel (advisor)
In this thesis deep convolutional neural networks models and feature descriptor models are compared. Feature descriptors are paired with suitable chosen classifier. These models are a part of machine learning therefore machine learning types are described in this thesis. Further these chosen models are described, and their basics and problems are explained. Hardware and software used for tests is listed and then test results and results summary is listed. Then comparison based on the validation accuracy and training time of these said models is done.
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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
|
|
|
Basics of Pedestrians Detection in Image by Machine Learning
Lučanský, Peter ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Táto Bakalárska práce sa zaoberá významnou problematikou v oblasti počítačového videnia, ktorou je detekcia osôb/chodcov v obraze, za pomoci metod strojového učenia, spolu s jej možným využitím, vývojom a vysvetlením princípov. Taktiež sa zaoberá testovaním dnes najlepšieho dostupného algoritmu, pričom sa porovnávajú faktory ktoré vplívajú na kvalitu jeho činnosti. Na začiatku je problematika stručne popísaná, potom sa prejde k podrobným popisom dosiahnutých pokrokov. V nasledujúcej časti sú popísané dostupné datasety, ktoré by sa dali použiť pri tréningu detekčného algoritmu. V poslednom rade sú vykonané trénovacie procesy za rozličných podmienok, pričom sú jednotlivé výsledky porovnávané.
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Image object detector based on C4 algorithm
Vylíčil, Radek ; Rajmic, Pavel (referee) ; Mašek, Jan (advisor)
This thesis deals with the image object detection using Contour cues. The text describes methods how to train and test object detector. The main contribution of this thesis consists in creation Feature extractor for creation object detector in Java programming. The functionality of object detector was demonstrated on medical images.
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