The target tracking using the spatial-temporal probability model

Summary form only given. Moving object extraction and tracking are the preliminary and fundamental processes in developing intelligent human-machine interaction and surveillance systems. In conventional target tracking systems, methods of background subtraction are applied to extract moving objects. However, a noisy image may be generated under a non-stationary background. Pixel-based temporal probability models are then proposed to reduce the noise effect, but the misalignment problem during target tracking on mosaic images makes the object extraction process inaccurate. We improve the method of using pixel-based temporal probability models by constructing spatial-temporal probability models to overcome the misalignment problem. Furthermore, the mosaic images are formed by stitching the images captured by an active camera with pan-tilt movements such that the proposed system can extract and track a moving object over a wide area in real-time.