Local Spatio-Temporal Propagation Based Adaptive Model Generation and Update for High Frame Rate and Ultra-Low Delay Foreground Detection

Abstract

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications, which demands better experience and higher accuracy. Foreground detection is an indispensable preprocessing step to make the system suitable for complex scenes. Although many foreground detection algorithms have been proposed, few can achieve high speed in hardware due to their high complexity or high consumption. Based on the foreground detection algorithm ViBe, this paper proposes a local spatio-temporal propagation based adaptive model generation and update strategy for high frame rate and ultra-low delay foreground detection. Our algorithm predicts whether a region is a foreground by setting up detecting points, thereby adaptively adjusting the number of pixels that needs to be modeled. Secondly, the local linear illumination correlation is used to update models, which makes the algorithm more robust to illumination changes. The evaluation results show that the proposed algorithm successfully achieves real-time processing on the field-programmable gate array (FPGA) at a resolution of $\mathbf{640}\times\mathbf{480}$ pixels, with a delay of 0.908ms/frame.