A comparative study of object detection models for Real Time Application in Surveillance Systems

Abstract

In this paper, we attempted to give an overview based on thorough research and test ing of the latest object detection methods with an aim to help developers to build a Real Time Responsive CCTV Camera Model. As we welcome the 5G network worldwide, the coming future will surely be heavily dependent on smart machines and internet-based technologies. Therefore, we can assume that our daily life secu rity will also be managed by smart devices. In this research work, our aim is to do a thorough research on the latest models so that one can be chosen to implement and minimize the existing security system into a one device depended security system. The device we often use for surveillance and security purpose is CCTV camera. However, most of the cameras are not connected to the internet also they are not responsive. Which means, the outputs from the cameras cannot be used for further analysis by machines and can only be saved for manual check by humans. Our re search will help to develop such a system that will make the camera act like more of a security guard itself rather than a video recording device only. As we need to find out the best suited detection method we will check the accuracy, implementation process, power usage, GPU and CPU usage and then choose between previously invented methods such as HOG (Histogram of Oriented Gradients), Viola Jones De tector or the latest inventions such as R-CNN, SSD YOLO. Finally, this research will help the security device developers to choose the best algorithm and build cost efficient systems. Also, the future works of the research will help to create alert for abnormal presence of unknowns under surveillance automatically. Overall, we can say that our research will help to build more affordable, efficient and digitally secured home, offices, schools or any other buildings and even roads and highways in coming days.