Multi-Temporal Foreground Detection In Videos

A common task in video processing is the binary separation of a video's content into either background or moving foreground. However, many situations require a foreground analysis with a finer temporal granularity, in particular for objects or people which remain immobile for a certain period of time. We propose an efficient method which detects foreground at different timescales, by exploiting the desirable theoretical and practical properties of Robust Principal Component Analysis. Our algorithm can be used in a variety of scenarios such as detecting people who have fallen in a video, or analysing the fluidity of road traffic, while avoiding costly computations needed for nearest neighbours searches or optical flow analysis. Finally, our algorithm has the useful ability to perform motion analysis without explicitly requiring computationally expensive motion estimation.