Visual tracking in cluttered environments is attractive and challenging. This paper establishes a probabilistic framework, called the visual probabilistic data-association filter (VPDAF), to deal with this problem. The algorithm is based on the probabilistic data-association method for estimating a true target from a cluster of measurements. There are two other key concepts which are involved in VPDAF. First, the sensor data are visual, similar to the target in the image space, which is a crucial property that should not be ignored in target estimation. Second, the traditional probabilistic data-association filter for the underlying application is vulnerable to stationary disturbances in image space, mainly due to some annoying background scenes which are rather similar to the target. Intuitively, such persistent noises should be separated out and cleared away from the continuous measurement data for seeking successful target detection. The proposed VPDAF framework, which incorporates template matching, can achieve the goal of reliable realtime visual tracking. To demonstrate the superiority of the system performance, extensive yet challenging experiments have been conducted