video surveillance

Towards Urban Public Safety: A Literature Review of Deep Learning Approaches for CCTV-based Violence Detection

Violence detection in video surveillance systems is a critical challenge for ensuring public safety in smart cities.  This study presents a comprehensive analysis of deep learning architectures and transfer learning techniques for violence detection, evaluating their performance across benchmark datasets, including Hockey Fight, Movies, and UCF-Crime.  ResNet50, MobileNetV2, ConvLSTM, DenseNet121, and Xception models are compared in terms of accuracy, computational efficiency, and real-world applicability.  Results highlight ResNet50 as the most effective model, achievi

FACE RECOGNITION METHODS IN VIDEO SURVEILLANCE SYSTEMS USING MACHINE LEARNING

The article is dedicated to the investigation of face identification methods and aims to determine the most suitable one for a security system based on facial recognition from surveillance cameras. The time costs of these methods and their robustness against geometric scale distortions and rotations in various planes have been analyzed. Custom datasets have been generated for experimentation purposes.

Дослідження методів виділення динамічних об’єктів у відеопослідовностях

This paper is devoted to the study of the most common background selection algorithms in video
sequences, methods of comparative analysis and quantitative characteristics for the selection of optimal
background selection algorithms. As a result of the conducted research the general indicator of efficiency
of work of algorithm of allocation of a background on the video sequences received from stationary
cameras of video surveillance in video surveillance systems is offered. A study of methods for detecting