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When the scene background is known and the intensity of moving objects contrasts with the intensity of the background, the objects are easily captured by exploiting occlusion, e.g., background-subtraction. However, when processing general scenes, the background is not known and researchers have mostly attempted to segment moving objects by using motion cues rather than occlusion. Since motion can only be accurately computed at highly textured regions, current motion segmentation methods either fail to segment low textured objects, or require expensive regularization techniques. We present a computationally simple algorithm and test it with segmentation of moving objects in low texture / low contrast videos that are obtained in low-light scenes. The images in the sequence are modeled taking into account the rigidity of the moving object and the occlusion of the background. We formulate the problem as the minimization of a penalized likelihood cost. Relaxation of the weight of the penalty term leads to a simple solution to the nonlinear minimization. We describe experiments that illustrate the good performance of our method.