Robust Spatial-Temporal Correlation Model For Background Initialization In Severe Scene

Scene background initialization is an important step as one low-layer method for high-layer applications in computer vision. However, this process is always affected by practical challenges such as illumination changes, background motion, camera jitter, intermittent movement and bad weather outdoors, etc. In this work, we develop a novel method called co-occurrence pixel-block (CPB) model via spatial-temporal correlation for robust background initialization. This work first introduces the CPB method for foreground extraction. And then, background information in spatial-temporal features are utilized to recover an adaptive background for the current frame. Experimental results obtained from the dataset of the challenging benchmark (SBMnet) validate it’s performance under various challenges.