Incorporating estimated motion in real-time background subtraction

Many existing background subtraction approaches model background color only and detect foreground as outliers, and hence may confuse background changes or noises with true foreground. We present a novel algorithm that utilizes motion cues computed from an optical flow algorithm. The additional motion information allows aligning moving foreground objects over time so that models can be built for foreground as well. It also facilities background (and foreground) modeling since both color and motion cues can be utilized. In practice, our GPU implementation is able to process QVGA-sized video sequences at 39.3 FPS on a laptop. Quantitative evaluation on standard testbeds demonstrate the competitive performance of our approach.