Exploring Rolling Shutter Effect for Motion Tracking with Objective Identification
Xiao Zhang, Griffin Klevering, Li Xiao · 2022
Sensing-based user interfaces hold enormous potential for smart homes, medical equipment, educational systems, AR/VR/MR, etc. However existing hand and body gesture recognition systems are mostly based on frame-level computer vision approaches, which have limitations such as the inability to operate in the environment with low brightness, short detection distance, without the objective identification ability, and coarse-grained tracking when the objectives are in high-speed motion. Therefore, in this paper, we propose to attach active LED elements on objectives and utilize rolling shutter effect to enhance the gesture recognition and achieve the fine-grained motion tracking with objective identification.