Optical Flow-Based Gait Modeling Algorithm for Pedestrian Navigation Using Smartphone Sensors


An optical flow-based mostly pedestrian gait modeling method integrating with perspective acquisition is proposed. The proposed methodology accomplishes online coaching of the gait model with displacement and frequency info whenever steps are detected. The displacement information inferred from optical flow is assigned adaptive weight to suppress outliers that arise from the pedestrian's feet and legs in the images. Moreover, a self-pruning linear regression mechanism is presented in gait modeling process to attenuate the adverse effects of abnormal samples. The experimental results demonstrate that the proposed technique can achieve better performance compared with the existing methods in terms of accuracy and efficiency. Furthermore, complex scenario experiments where the textures of the ground changed, were conjointly conducted and the results verified the adaptability of our proposed methodology.

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