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Vehicle Counting in Still Image
Vágner, Filip ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this work is to compare models of convolutional neural networks designed to count vehicles in a static image using density estimation with a focus on different sizes of objects in the scene. A total of four models were evaluated - Scale Pyramid Network, Scale-adaptive CNN, Multi-scale fusion network and CASA-Crowd. The evaluation was done on three data sets - TRANCOS, CARPK, PUCPR+. Scale Pyramid Network achieved the best results. The model reached 5.44 in the Mean Absolute Error metric and 9.95 in the GAME(3) metric on TRANCOS dataset.
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Vehicle Counting in Still Image
Hladiš, Martin ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this thesis is to compare different models of convolutional neural networks, which use the principle of using density estimation to count the number of vehicles in a still image. The tested models were -- Counting CNN, Scale-adaptive CNN, Multi-Scale Fusion Net a Multi-scale CNN. Their estimation capability was tested using these datasets -- TRANCOS, CARPK, PUCPR+. The most accurate results were achieved by the Multi-Scale Fusion Net model. Its estimation accuracy using the dataset TRANCOS in the Mean Absolute Error metric achieved value of 8.05.
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Vehicle Counting in Still Image
Vágner, Filip ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this work is to compare models of convolutional neural networks designed to count vehicles in a static image using density estimation with a focus on different sizes of objects in the scene. A total of four models were evaluated - Scale Pyramid Network, Scale-adaptive CNN, Multi-scale fusion network and CASA-Crowd. The evaluation was done on three data sets - TRANCOS, CARPK, PUCPR+. Scale Pyramid Network achieved the best results. The model reached 5.44 in the Mean Absolute Error metric and 9.95 in the GAME(3) metric on TRANCOS dataset.
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Vehicle Counting in Still Image
Hladiš, Martin ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this thesis is to compare different models of convolutional neural networks, which use the principle of using density estimation to count the number of vehicles in a still image. The tested models were -- Counting CNN, Scale-adaptive CNN, Multi-Scale Fusion Net a Multi-scale CNN. Their estimation capability was tested using these datasets -- TRANCOS, CARPK, PUCPR+. The most accurate results were achieved by the Multi-Scale Fusion Net model. Its estimation accuracy using the dataset TRANCOS in the Mean Absolute Error metric achieved value of 8.05.
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