Light-weight salient foreground detection with adaptive memory requirement

Designing algorithms, which require less memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total memory requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of memory for every pixel, we allocate less memory for stable background pixels. The plot of the required memory at each frame also serves as a tool to find the video portions with high activity.