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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
Jelínek, Zdeněk ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The main goal of this thesis was to compare different approaches to vehicle counting by density estimation. Four convolutional neural networks were tested - Counting CNN, Hydra CNN, Perspective-Aware CNN and Multi-column CNN. The evaluation of these models was done on three different datasets. The Perspective-aware CNN has achieved the most accurate results across all datasets. This model has reached 2.86 Mean Absolute Error on the PUCPR+ dataset, proving that it is the most suitable for the vehicle counting problem.
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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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Vehicle Counting in Still Image
Jelínek, Zdeněk ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The main goal of this thesis was to compare different approaches to vehicle counting by density estimation. Four convolutional neural networks were tested - Counting CNN, Hydra CNN, Perspective-Aware CNN and Multi-column CNN. The evaluation of these models was done on three different datasets. The Perspective-aware CNN has achieved the most accurate results across all datasets. This model has reached 2.86 Mean Absolute Error on the PUCPR+ dataset, proving that it is the most suitable for the vehicle counting problem.
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Orthogonal series density estimation
Zheng, Ci Jie ; Dvořák, Jiří (advisor) ; Pawlas, Zbyněk (referee)
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize them into a parametric and a nonparametric methodology. Examples of a nonparametric density estimation are histogram and kernel density estimation. Another example of the nonparametric methodology is orthogonal series density estimation. In this work, we will describe the fundamental idea behind this methodology. We will also show how Kronmal-Tarter method estimates the density of known underlying data.
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Nonparametric density estimates used for multiple AE source detection
Gális, P. ; Chlada, Milan
Paper deals with the localization of acoustic emission (AE) sources by means of exact geodesic curves on 3D vessels composed of several parametrized surfaces. Precise arrival times of AE events, plugged in DeltaT/DeltaL localization is discussed. Two-dimensional nonparametric (kernel) density estimates are employed to obtain the localization maps on 3-D surfaces and the potential regions of cracks are visibly highlighted. This newly proposed procedure is applied to steam reservoir and other experimental vessels. The results presented are accompanied by thoroughgoing evaluation of their quality and practical usefulness.
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The density estimation of Large carnivores in the selected parts of West Carpathians and factors affecting their occurance
Kuruganti, Shaldayya
The study showed that density estimation of Eurasian lynx corresponds to 1.3 and 1.2 independent individuals per 100 km2 in the Jvorniky study area for the two time periods and 0.8 independent individuals per 100 km2 for Beskydy study area. The study failed to identify other large carnivores such a wolf (Canis lupus) and bear (Ursus arctos) from both Beskydy and Javorniky study areas. The estimated density of Lynx is low and their numbers should increase in future. There is enough prey base to support the existing population in the two study areas. The main factors effecting Lynx distribution are habitat fragmentation, poaching by humans, depleting the prey base by over hunting leading to starvation, vehicle collisions. Strict measures should be implemented to protect the species and long term study programmes must be started to get a comprehensive knowledge about the biology of species. Reintroductions must be carried over where there are suitable habitat for the survival and propagation of Lynx. The reason for not detecting wolf or bear might be due to the fact that the study areas are wide and the few migrating wolf or bear might be present outside my study area. Also there is lot of possibility to reintroduce wolf in my study area and I hope this will be done in future to ensure better biodiversity and to ensure wildlife conservation.
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