Robust Foreground Detection Using Smoothness and Arbitrariness Constraints

Foreground detection plays a core role in a wide spectrum of applications such as tracking and behavior analysis. It, especially for videos captured by fixed cameras, can be posed as a component decomposition problem, the background of which is typically assumed to lie in a low dimensional subspace. However, in real world cases, dynamic backgrounds like waving trees and water ripples violate the assumption. Besides, noises caused by the image capturing process and, camouflage and lingering foreground objects would also significantly increase the difficulty of accurate foreground detection. That is to say, simply imposing the correlation constraint on the background is no longer sufficient for such cases. To overcome the difficulties mentioned above, this paper proposes to further take into account foreground characteristics including 1) the smoothness: the foreground object should appear coherently in spatial domain and move smoothly in temporal, and 2) the arbitrariness: the appearance of foreground could be with arbitrary colors or intensities. With the consideration of the smoothness and the arbitrariness of foreground as well as the correlation of (static) background, we formulate the problem in a unified framework from a probabilistic perspective, and design an effective algorithm to seek the optimal solution. Experimental results on both synthetic and real data demonstrate the clear advantages of our method compared to the state of the art alternatives.