This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF) with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedure is employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assisted calibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments. Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KF-based approach with the landmark information can calibrate the location estimation and reduce the corner effect of a location-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimental results demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.