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Whereas the other localization techniques are still limited in distance, accuracy (e.g., WIFI, RFID), or limited in setting the environment and usability (e.g., Bluetooth, GPS). The vision–based localization technique, particularly, in indoor-environment has many advantages such as: scalable, high accuracy; without requirements of the additional attached-equipment/devices to the subjects. Due to such advantages, this thesis aims to study, and propose a high accuracy vision-based localization system for moving human in indoor environments. To achieve a high accuracy positioning system, the thesis deals with the critical issues of a vision-based localization. We observe that there is no a perfect human detector and tracking. Then we utilize a regression-model to eliminate outlier detections. Throughout the thesis, we first briefly introduce an overview of vision – based localization. We then present the proposed frame-work including steps: Background Subtraction for detecting moving subject; shadows removal techniques for improving detection result, and linear regression method to eliminate the outliers; and finally the tracking object using a Kalman Filter. The most important result of the thesis is demonstrations which show a high accuracy and real-time computation for human positioning in indoor environments. These evaluations are implemented in several indoor environments with different lighting conditions, human appearances to confirm robustness of the system.