Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery

TL;DR

This paper introduces a novel Bayesian Gaussian mixture model for background subtraction in thermal imagery, enabling automatic adaptation to changing conditions and efficient hardware implementation for real-time surveillance.

Contribution

It presents a new thermal-specific background subtraction method using Bayesian Gaussian mixtures with automatic structure estimation and hardware acceleration.

Findings

High accuracy in thermal background modeling

Automatic adaptation to dynamic environments

Efficient hardware implementation with low power consumption

Abstract

Detection of moving objects in videos is a crucial step towards successful surveillance and monitoring applications. A key component for such tasks is called background subtraction and tries to extract regions of interest from the image background for further processing or action. For this reason, its accuracy and real-time performance is of great significance. Although, effective background subtraction methods have been proposed, only a few of them take into consideration the special characteristics of thermal imagery. In this work, we propose a background subtraction scheme, which models the thermal responses of each pixel as a mixture of Gaussians with unknown number of components. Following a Bayesian approach, our method automatically estimates the mixture structure, while simultaneously it avoids over/under fitting. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing operation conditions. We propose a reference implementation of our method in reconfigurable hardware achieving both adequate performance and low power consumption. Adopting a High Level Synthesis design, demanding floating point arithmetic operations are mapped in reconfigurable hardware; demonstrating fast-prototyping and on-field customization at the same time.