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Because safety is important in everyday life, according to the National Retail Federation, organized retail theft accounts for an estimated 30 billion dollars each year in the U.S. Therefore, this paper proposes the detection system of theft walking patterns using motion-based Artificial Intelligence from a stationary camera. This method uses object detection by the Yolov5 model for localization of the objects, which is less affect to a background problem to detect the moving objects from the RGB video. After that, the coordinates of bounding boxes from Yolov5 are used to calculate the centroid of the box; then, it is used to make the motion of walking pattern behaviour. Finally, the sequences of the motion patterns are used with a bi-directional long-short term memory network, which is used for the time-independent pattern using 10-fold cross validation at training size 50% of total dataset. To evaluate the performance of the method, the datasets consist of normal walking people, which have 30 videos, and theft walking people, which have 30 videos. The accuracy of the system is approximately 97%.

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