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An optical flow-based pedestrian gait modeling method integrating with attitude acquisition is proposed. The proposed method accomplishes online training of the gait model with displacement and frequency information 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 method 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 also conducted and the results verified the adaptability of our proposed method.
Qian et al. (Tue,) studied this question.