Efficient reduction of computational complexity in video surveillance using hybrid machine learning - for event recognition

Aug 2, 2026

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High-resolution video streams in surveillance systems create computational complexity that limits real-time responsiveness, scalability, and performance. The paper proposes an efficient hybrid machine learning approach for event recognition by integrating convolutional neural networks and recurrent models to improve detection accuracy while reducing processing time and energy use. The method is benchmarked against motion detection, background subtraction, and frame differencing, showing gains in frame processing time, object detection speed, anomaly detection accuracy, and real-time data processing, further optimized with dynamic model scaling and edge computing for practical deployment.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Article Info

ABSTRACT

1. INTRODUCTION

Efficient reduction of computational complexity in video surveillance using hybrid machine learning for event recognition Jyothi Honnegowda1, Komala Mallikarjunaiah1, Mallikarjunaswamy Srikantaswamy2 1Department of Electronics and Communication Engineering, SJB Institute of Technology, Bengaluru, India. 2Department of Electronics and Communivation Engineering, JSS Academy of Technical Education, Bengaluru, India | Article history:
Received Mar 16, 2024 Revised Jul 11, 2024 Accepted Jul 26, 2024 | This paper addresses the challenge of high computational complexity in video surveillance systems by proposing an efficient model that integrates hybrid machine learning algorithms (HML) for event recognition. Conventional surveillance methods struggle with processing vast amounts of video data in real-time, leading to scalability, and performance issues. Our proposed approach utilizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the accuracy and efficiency of detecting events. By comparing our model with conventional surveillance techniques motion detection, background subtraction, and frame differencing. We demonstrate significant improvements in frame processing time, object detection speed, energy efficiency, and anomaly detection accuracy. The integration of dynamic model scaling and edge computing further optimizes computational resource usage, making our method a scalable and effective solution for realtime surveillance needs. This research highlights the potential of machine learning to revolutionize video surveillance, offering insights into developing more intelligent and responsive security systems. The results of your simulation analysis, indicating performance improvements in accuracy by 0.25%, 0.35%, and 0.45% for the motion detection algorithm, background subtraction, and frame differencing respectively, and in real-time data processing by 5.65%, 4.45%, and 6.75% for the motion detection algorithm, background subtraction, and frame differencing respectively, highlight the potential of machine learning to transform video surveillance into a more intelligent and responsive system.
This is an open access article under the CC BY-SA license.
| | --- | --- | | Keywords:
Computational complexity reduction
Deep learning
Event recognition Machine learning algorithms Real-time processing Video surveillance | | Corresponding Author: Mallikarjunaswamy Srikantaswamy Department of Electronics and Communication Engineering, JSS Academy of Technical Eductaion Bengaluru 560060, India [Email: pruthvi.malli@gmail.com](Email: pruthvi.malli@gmail.com) Article Info ABSTRACT 1. INTRODUCTION Video surveillance systems have become integral to maintaining security and monitoring activities in public spaces, traffic management, and private sectors. With the advent of digital technology, these systems have evolved from simple video recording devices to complex networks capable of analyzing and interpreting vast amounts of visual data in real time. The capability to automatically recognize specific events or behaviors within this data has significant implications for safety, efficiency, and resource management. However, the effectiveness of these systems is often constrained by the computational complexity involved in processing high-resolution video streams, leading to challenges in scalability, and real-time responsiveness [1], [2] . Recent trends in the field have seen a shift towards leveraging machine learning algorithms to enhance the capabilities of video surveillance systems. These advancements enable the automation of event recognition, allowing for quicker and more accurate identification of incidents or activities of interest. Machine learning, particularly deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has shown promise in deciphering complex patterns in video data that would be impractical for

FAQs

What problem does the paper target in video surveillance systems?

It targets the high computational complexity of processing high-resolution video streams, which restricts scalability and real-time responsiveness.

What hybrid machine learning components does the proposed method use?

The approach integrates convolutional neural networks (CNNs) for spatial feature analysis and recurrent models (including LSTM) for sequence-based event classification.

How does the proposed model compare with conventional surveillance techniques?

It outperforms motion detection, background subtraction, and frame differencing in frame processing time, object detection speed, energy efficiency, and anomaly detection accuracy, with reported improvements in simulation and real-time processing.

Aug 2, 2026

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