National Repository of Grey Literature 45 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Object detection for video surveillance using the SSD approach
Dobranský, Marek ; Lokoč, Jakub (advisor) ; Božovský, Petr (referee)
The surveillance cameras serve various purposes ranging from security to traffic monitoring and marketing. However, with the increasing quantity of utilized cameras, manual video monitoring has become too laborious. In re- cent years, a lot of development in artificial intelligence has been focused on processing the video data automatically and then outputting the desired no- tifications and statistics. This thesis studies the state-of-the-art deep learning models for object detection in a surveillance video and takes an in-depth look at SSD architecture. We aim to enhance the performance of SSD by updating its underlying feature extraction network. We propose to replace the initially used VGG model by a selection of modern ResNet, Xception and NASNet classifica- tion networks. The experiments show that the ResNet50 model offers the best trade-off between speed and precision, while significantly outperforming VGG. With a series of modifications, we improved the Xception model to match the ResNet performance. On top of the architecture-based improvements, we ana- lyze the relationship between SSD and a number of detected classes and their selection. We also designed and implemented a new detector with the use of temporal context provided by the video frames. This detector delivers enhanced precision while...
Application for automatic recognition of textures in map data
Šípoš, Peter ; Skopal, Tomáš (advisor) ; Lokoč, Jakub (referee)
This work has aimed to implement an easy-to-use application which can be used to navigate through aerial imagery, assign sections of this image for different classes. Based on these category assignments the application can autonomously assign categories to so-far unknown fields, hence it helps the user in further classification. The output of the application is an index file, which can serve as underlying dataset for further analysis of a given area from geographic or economic point-of-view. To fulfil this task the program uses standard MPEG-7 descriptors to perform the feature extraction upon which the classification relies.
Zlepšení metod rozpoznávání obličejů s pomocí senzoru pro sledování pohybu těla
Belák, Michal ; Kruliš, Martin (advisor) ; Lokoč, Jakub (referee)
Kinect is a motion tracking input device developed by Microsoft. It uses a variety of on-board sensors to efficiently find and track human bodies represented in skeletal models. It was developed primarily to facilitate controlling of the Xbox 360 gaming console using body movement. Microsoft also released a software development kit for Kinect, giving programmers access to both raw data streams and computed tracking data from a connected Kinect. A second version of Kinect was released for Xbox One, bringing various hardware improvements as well as a new and incompatible version of the SDK. The main objective of this thesis is to determine, whether Kinect may be a better platform for real-time face recognition than regular cameras. We developed software compatible with both versions of Kinect, combining Kinect tracking data with existing face recognition libraries. We used this software to assess the benefits of tracking data by empirical evaluation on collected data.
Known-Item Search in Image Datasets Using Automatically Detected Keywords
Souček, Tomáš ; Lokoč, Jakub (advisor) ; Peška, Ladislav (referee)
Known-item search represents a scenario, where a user searches for one particular image in a given collection but does not know where it is located. The thesis focuses on the design and evaluation of a keyword retrieval model for known-item search in image collections. We use a deep neural network trained on a custom dataset to annotate the images. We design complex yet easy-to-use query interface for fast image retrieval. We use/design several types of artificial users to estimate the model's performance in an interactive setting. We also discuss our successful participation at two international competitions. 1
Environment for Lifting
Kubový, Jan ; Pelikán, Josef (advisor) ; Lokoč, Jakub (referee)
The aim of the thesis is to create a library that will provide ease way to cre- ating and experimenting with computing networks. The concpet of computing netowork can be explained as algorithms whitch can be devided into small simple parts (nodes). The main focus of this library is to easily experiment with trans- formations based on lifting. There are inverse operations for these connections, which are used for lossless compression of data or signal. Emphasis was put on the simplicity of creating new nodes and subsequent connections. An integral part of the work is also an example of several transformations based on lifting.
Visual Question Answering
Hajič, Jakub ; Straka, Milan (advisor) ; Lokoč, Jakub (referee)
Visual Question Answering (VQA) is a recently proposed multimodal task in the general area of machine learning. The input to this task consists of a single image and an associated natural language question, and the output is the answer to that question. In this thesis we propose two incremental modifications to an existing model which won the VQA Challenge in 2016 using multimodal compact bilinear pooling (MCB), a novel way of combining modalities. First, we added the language attention mechanism, and on top of that we introduce an image attention mechanism focusing on objects detected in the image ("region attention"). We also experiment with ways of combining these in a single end- to-end model. The thesis describes the MCB model and our extensions and their two different implementations, and evaluates them on the original VQA challenge dataset for direct comparison with the original work. 1
Comparison of signature-based and semantic similarity models
Kovalčík, Gregor ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
Content-based image retrieval and similarity search has been investigated for several decades with many different approaches proposed. This thesis fo- cuses on a comparison of two orthogonal similarity models on two different im- age retrieval tasks. More specifically, traditional image representation models based on feature signatures are compared with models based on state-of-the-art deep convolutional neural networks. Query-by-example benchmarking and tar- get browsing tasks were selected for the comparison. In a thorough experimental evaluation, we confirm that models based on deep convolutional neural networks outperform the traditional models. However, in the target browsing scenario, we show that the traditional models could still represent an effective option. We have also implemented a feature signature extractor into the OpenCV library in order to make the source codes available for the image retrieval and computer vision community. 1
Effective visualization for interactive video exploration
Pavlovský, Jan ; Lokoč, Jakub (advisor) ; Grošup, Tomáš (referee)
In this thesis we introduce an innovative approach to visualisation and search results presentation for large video collection search and browsing. The general problem of video search is analysed and discussed in comparison with other current software tools and methods used for video search. A specific visualisa- tion method and algorithm for its generation is then proposed and discussed. We evaluated the methods both, empirically and by a user study. Based on the results, we chose the best possible algorithm settings for interactive video search and applied them. A simple experimental software tool implementing the proposed methods is developed focusing on the visualisation components. 1
Aplikace umělých neuronových sítí pro detekci malware v HTTPS komunikaci
Bodnár, Jan ; Lokoč, Jakub (advisor) ; Somol, Petr (referee)
A huge proportion of modern malicious software uses Internet connec- tions. Therefore, it is possible to detect infected computers by inspecting network activity. Since attackers hide the content of communication by com- municating over encrypted protocols such as HTTPS, communication must be analysed purely on the basis of metadata. Cisco provided us a dataset containing aggregated metadata with additional information as to whether or not each sample contains malicious communication. This work trains neu- ral networks to distinguish between infected and benign samples, comparing different architectures of neural networks and providing a comparison with results achieved by different machine learning methods tried by colleagues. It also seeks to create a mapping which maps samples of communication into a space where different samples of malicious communication created by a sin- gle malware family form clusters. This may make it easier to find different computers infected by a virus with known behaviour, even when the virus cannot be detected by the detection system. 1
Efficient kNN classification of malware from HTTPS data
Maroušek, Jakub ; Lokoč, Jakub (advisor) ; Galamboš, Leo (referee)
An important task of Network Intrusion Detection Systems (NIDS) is to detect malign com- munication in a computer network traffic. The traditional detection approaches which analyze the content of network packets, are becoming insufficient with an increased usage of encrypted HTTPS protocol. The previous research shows, however, that the high-level properties of HTTPS commu- nication such as the duration of a request or the number of bytes sent/received from the client to the server may be successfully used to detect behavioral patterns of malware activity. We study approximate k-NN similarity joins as one of the methods to build a classifier recognizing malign communication. Three MapReduce-based and one centralized approximate k-NN join methods are reimplemented in order to support large volumes of high-dimensional data. Finally, we thoroughly evaluate all methods on different datasets containing vectors up to 1000 dimensions and compare multiple aspects concerning scalability, approximation precision and classification precision of each approach.

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