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Smart Surveillance System Using Deep Learning Approaches
Abstract
In modern days, CCTVs are being used for monitoring, and most shops have surveillance cameras up and running during the night times, but still, robberies are happening since the surveillance footage is being checked only after a robbery on the next day. To overcome the problems of having manual security and cost wastage along with automating the monitoring of the surveillance during the night times once the shops are closed, the authors propose the smart surveillance system. Deep learning algorithms and computer vision techniques are used to detect the presence of humans/intruders in a given video. The smart surveillance system along with the reduction in the cost of manual securities also provides robust nighttime monitoring, and it provides immediate notification to the authority as soon as it spots the intruder in the specified monitoring time, thereby reducing the robberies and the business impact caused.
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