Abstract: A method of combining multiple moving objects prediction schemes is presented that allows a tracking framework to select and identify the best observation evidence in occlusion scenarios. The underlying framework tracks any objects in monocular image sequences taken from stationary uncalibrated cameras with fixed focal length. A mixture model method is deployed to estimate the static background reference image. The tracking algorithm simply uses a constant acceleration motion model to track objects in the simplest scenarios. However, the main contribution is the use of three simultaneous predictors with a least square correlation stage to select the most likely object position. The three prediction schemes are an - tracking scheme, a Kalman filtering method, and a region segmentation and matching method. The tracker is evaluated against different image sequences each offering different occlusion problems.