Realtime Object Detection Through M-ResNet in Video Surveillance System  

作　　者：S.Prabu J.M.Gnanasekar 

机构地区：[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

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2257-2271,共15页智能自动化与软计算（英文）

摘　　要：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,monitor-ing 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 high-er 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 segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.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 preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

关 键 词：Object detection ResNet video survilence image processing object quality 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]