Human Gait Detection Method Based on Binocular Vision for Virtual Reality Omnidirectional Treadmill

Authentic walking experience is a key issue for immersive virtual worlds. The omnidirectional treadmill (ODT) is an effective solution to realize the virtual reality free walking experience in a limited indoor space, and real-time human positioning is a crucial component of this solution. However, existing strategies often do not combine ODT and human positioning systems well, there are problems in cost, anti-interference, accuracy and so on. In this paper, we propose a lightweight method for human gait detection on a virtual reality ODT. Our method combines the characteristics of the ODT by using infrared markers, image processing, improved Kalman filter and other algorithms to obtain real-time posture of a mobile binocular camera and gait information. The experimental results show that this method can effectively track the corresponding gait information of users’ feet and waist when walking freely on ODT. The detection accuracy can reach millimeter level, and this information can be used for subsequent control of ODT, thus effectively solving the movement problem in indoor virtual reality. Compared with other methods, our approach has several advantages, including low cost, no need for additional calibration, anti-interference, no additional space occupation and convenient use. And it avoids the defect of recalibration required for positioning base station movement in other methods.