Open AccessDevelopment of an algorithm for abnormal human behavior detection in intelligent video surveillance system
Author(s) -
Dina Satybaldina,
Natalya Glazyrina,
Gulziya Kalymova,
V S Stepanov
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1069/1/012046
Subject(s) - computer science , gesture , artificial intelligence , artificial neural network , software , gesture recognition , computer vision , object (grammar) , object detection , deep learning , selection (genetic algorithm) , architecture , cognitive neuroscience of visual object recognition , pattern recognition (psychology) , programming language , art , visual arts
The aim of the work is to develop algorithms for analyzing video data in real time based on computer vision methods, as well as deep learning technologies for artificial neural networks for abnormal human behavior recognition near critical facilities using ATMs as an example. The article provides an overview of the initial research aimed at the choice of data capture devices, neural network architecture, software implementation and selection of experimental conditions (distance and illumination). Static and dynamic hand gestures were used as object’s movements. Experimental results show that using the Intel RealSense D435 Depth Camera provides more accurate dynamic gesture recognition under different experimental conditions.