Foreground Object Detection Using Top-Down Information Based on EM Framework

In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed. This foreground model can augment the foreground detection for the camouflage problem. Thus, an object's state-specific Markov random field (MRF) model is constructed for detection based on the foreground model and the background model. This MRF model depends on the latent variables that describe each object's state. The maximization of the MRF model is the M-step in the EM framework. Besides fusing spatial information, this MRF model can also adjust the contribution of the top-down information for detection. To obtain detection result using this MRF model, sampling importance resampling is used to sample the latent variable and the EM framework refines the detection iteratively. Besides the proposed generalized EM framework, our method does not need any prior information of the moving object, because we use the detection result of moving object to incorporate the domain knowledge of the object shapes into the construction of top-down information. Moreover, in our method, a kernel density estimation (KDE)-Gaussian mixture model (GMM) hybrid model is proposed to construct the probability density function of background and moving object model. For the background model, it has some advantages over GMM- and KDE-based methods. Experimental results demonstrate the capability of our method, particularly in handling the camouflage problem.