A novel background subtraction scheme for in-camera acceleration in thermal imagery

Real-time segmentation of moving regions in image sequences is a very important task in numerous surveillance and monitoring applications. A common approach for such tasks is the “background subtraction” which tries to extract regions of interest from the image background for further processing or action; as a result its accuracy as well as its real-time performance is of great significance. In this work we utilize a novel scheme, designed and optimized for FPGA-based implementations, which models the intensities of each pixel as a mixture of Gaussian components; following a Bayesian approach, our method automatically estimates the number of Gaussian components as well as their parameters. Our novel system is based on an efficient and highly accurate on-line updating mechanism, which permits our system to be automatically adapted to dynamically changing operation conditions, while it avoids over/under fitting. We also present two reference implementations of our Background Subtraction Parallel System (BSPS) in Reconfigurable Hardware achieving both high performance as well as low power consumption; the presented FPGA-based systems significantly outperform a multi-core ARM and two multi-core low power Intel CPUs in terms of energy consumed per processed pixel as well as frames per second. Moreover, our low-cost, low-power devices allow for the implementation, for the first time, of a highly distributed surveillance system which will alleviate the main problems of the existing centralized approaches.