Abstract
Nowadays, there is no place where security cameras (CCTV) are not used. Security cameras play a huge role in solving criminal cases. However, a lot of time is spent examining these camera recordings. This situation causes the incidents to not be resolved and causes delays. This study, it is aimed to use machine learning to increase the size of security camera recordings with efficient algorithms that can work on devices with low processing power such as embedded systems. Within the scope of the study, an experimental environment was created by installing a security camera system. Fast and effective video reduction algorithms have been developed on videos collected in different scenarios. New approaches called hopscotch and lens algorithms have been presented for video reduction. These approaches are aimed to obtain rapid results by applying them to security camera videos. It is thought that the developed video reduction approaches will lead to the creation of applicable prototypes on embedded cards such as Raspberry Pi. PSNR (Peak Signal to Noise Ratio) metric was used to compare the images. Real-time results were obtained with our approaches applied to images.
Published Version
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