Open Access

ARTICLE

Realtime Object Detection Through M-ResNet in Video Surveillance System

S. Prabu^1,*, J. M. Gnanasekar²

1 Department of Computer Science & Engineering, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, Tamilnadu, 603308, India
2 Department of Computer Science & Engineering, Sri Venkateshwara College of Engineering & Technology, Chennai, 602001, Tamilnadu, India

* Corresponding Author: S. Prabu. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2257-2271. https://doi.org/10.32604/iasc.2023.029877

Received 14 March 2022; Accepted 19 April 2022; Issue published 19 July 2022

Abstract

Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images pixel by pixel. In digital image processing, segmentation is the process of segregating the individual image parts into pixels. The performance of segmentation is affected by irregular illumination and/or low illumination. These factors highly affect the real-time object detection process in the video surveillance system. In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light. Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream. The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.

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Keywords

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