Adaptive human tracking for smart wheelchair

People with impairment and having difficulties to walk, even impossible to move due to illness, injury, or disability need assistance tool. One assistance tool to help those people is wheelchair. With current technological developments, conventional wheelchair can be improved. Conventional wheelchair which operated by hand cannot be used by people with hand-foot impairment, as well as electric-powered wheelchair that need to be controlled with hand. For those with hand-foot impairment, conventional wheelchair can be assisted by assistant to help pushing and to maneuver. One drawback with this approach is the assistant will have limited movement and will have fatigue from pushing a wheelchair. This research try to overcome this drawback so that the wheelchair can move semi-autonomously. Proposed approach incorporates human tracking algorithm that later will be used to make the wheelchair moving independently without assistant to push from behind. This paper propose a framework that combines keypoint descriptors for human tracking: ORB, KAZE, AKAZE, BRISK, SIFT, and SURF. Each keypoint descriptors are given a score which is used to choose which descriptor is used until the minimum number of keypoints is fulfilled. If the best in the method list does not suffice, then the second best will be selected to generate keypoints, and so on. The result of the framework obtained high precision, 0.93 and 0.89 from two videos with different environments.