Unsupervised motion detection using a Markovian temporal model with global spatial constraints

In this work, we propose an unsupervised Bayesian model for the detection of moving objects from dynamic scenes. This unsupervised solution is a three-step approach that uses a statistical model of an interframe gradient norm field (as likelihood model) with a local regularization term (as prior model) combined with strong intraframe spatial constraints. In the first step, the spatial constraints are estimated by making an unsupervised Markovian spatial over-segmentation of two input frames. In the second step, the interframe gradient (derived from the input frames) is restored to minimize undesired noise. In the last step, an unsupervised Markovian temporal segmentation (with global spatial constraints) is performed to generate the desired motion label field. The maximum a posteriori (MAP) estimation of the label field associated with the spatial segmentations (in the first step) and the motion label field (in the third step) is performed by a classical Iterative Conditional Mode (ICM) algorithm. An Iterative Conditional Estimation (ICE) procedure is exploited for estimating the parameters of the spatial model and the region-constrained temporal model. This new statistical method of motion detection has been successfully applied to real dynamic scenes and seems to be well suited for the temporal detection of noisy image sequences.