Thesis Details
Detekce anomálií v chůzi chodců
The goal of this work was to create a system that would be able to detect anomalies in pedestrian walking. As the core of my application, I have used OpenPose, which is an application for detecting human skeletons. Then I used a bidirectional LSTM neural network to detect anomalies in video sequences. This architecture was chosen during the experiment because it outperformed other solutions. I trained my model to detect three types of anomalies. The output of my application is a video with marked sequences of anomalies. The whole system is implemented in Python.
machine learning, artificial intelligence, neural networks, anomaly, anomaly detection, image processing, computer vision, human detection, skelet detection, python, LSTM, reccurent neural networks, surveillance system
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT24839,
author = "Ond\v{r}ej Pokorn\'{y}",
type = "Bachelor's thesis",
title = "Detekce anom\'{a}li\'{i} v ch\r{u}zi chodc\r{u}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/24839/"
}