Multi-Object Detection for Real-time Video Surveillance Systems

Authors

https://doi.org/10.48314/ceti.v1i1.21

Abstract

Video surveillance systems play a crucial role in modern security, with real-time applications becoming increasingly important across various sectors. This paper proposes a novel method for detecting moving objects in real time using Convolutional Neural Networks (CNNs) combined with Gaussian Mixture Modeling (GMM) for background subtraction. The system processes video sequences, converting them into frames where moving objects are distinguished from the background. GMM is used to model the background, while CNNs extracts local optimal features to enhance object tracking accuracy. This method is particularly effective for applications such as operational robotics and military surveillance, which require real-time mobile detection systems. The proposed system was tested using custom video sequences, demonstrating significant improvements in object detection accuracy and reliability even in challenging conditions such as low-light environments. The results show improved performance in tracking moving objects, with the system successfully reducing noise and maintaining detection precision.

Keywords:

Real-time video surveillance, Multi-object detection, Convolutional neural networks, Gaussian mixture model, Background subtraction, Mobile surveillance systems, Video sequence processing, Real-time detection, Low-light object detection

References

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Published

2024-02-18

Issue

Vol. 1 No. 1 (2024): Computational Engineering and Technology Innovations

Section

Articles

How to Cite

Rasoulpour *, F., & Milovancevic, M. (2024). Multi-Object Detection for Real-time Video Surveillance Systems. Computational Engineering and Technology Innovations, 1(1), 11-24. https://doi.org/10.48314/ceti.v1i1.21

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