Deep learning-based security system powered by low cost hardware and panoramic cameras

Abstract

Automatic video surveillance systems are usually designedto detect anomalous objects being present in a scene or behaving dangerously.In order to perform adequately, they must incorporate modelsable to achieve accurate pattern recognition in an image, and deeplearning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a 360 ◦camera. The decision about the block of the image to analyze isbased on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a RaspberryPi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.