Adaptive background estimation: Computing a pixel-wise learning rate from local confidence and global correlation value

Adaptive background techniques are useful for a wide spectrum of applications, ranging from security surveillance, traffic monitoring to medical and space imaging. With a properly estimated background, moving or new objects can be easily detected and tracked. Existing techniques are not suitable for real-world implementation, either because they are slow or because they do not perform well in the presence of frequent outliers or camera motion. We address the issue by computing a learning rate for each pixel, a function of a local confidence value that estimates whether a pixel is (or not) an outlier, and a global correlation value that detects camera motion. After discussing the role of each parameter, we report experimental results, showing that our technique is fast but efficient, even in a real-world situation. Furthermore, we show that the same method applies equally well to a 3-camera stereoscopic system for depth perception.