Detecting foreground disambiguation of depth images using fuzzy logic

We present a unique occlusion and foreground overlap detection technique from depth sensor data using a fuzzy rule-based system. Features such as bounding box parameters and skeletonization were extracted from the foreground images and then input to the Fuzzy Inference System. Overlap and occlusion confidence measures were taken for each frame in the image sequence and compared against the extracted ground truth. This technique can help filter out occluded regions in the image sequence which, in an Eldercare environment, can then be used to compute accurate estimates of fall risk parameters such as stride time, stride length, and walking speed on a daily basis in in order to monitor the well-being of older adults in an ambient assisted living facility.