An Iterative Localization Method for Feedback-Based Advanced Feature Pattern Recognition

Traditional pattern recognition methods face challenges in pedestrian localization. In complex motion environments, failure to consider diversity, continuity and randomness of pedestrian gait can cause inaccurate localization. In this article, a feedback feature learning system is designed to improve human motion recognition and localization accuracy. First, advanced features are extracted using out-of-bag (OOB) evaluation, and optimal motion patterns are extracted using frequency pie probability, with an average recognition accuracy as high as 99.8%. Then, to enhance the accuracy of zero-velocity update (ZUPT) at different step frequencies, a novel peak detection method is developed to extract real-time stride frequency, and an adaptive step frequency-based ZUPT optimal threshold function is obtain-ed. Next, a motion constraint model is designed to overcome the influence of non-Gaussian noise, and the 3-D position information in different motion modes solved by Extended Kalman filter (EKF) is optimized. Finally, the obtained more accurate 3-D position information is used as the new feature input of the classifier to further improve the pattern recognition accuracy. After three feature iteration feedbacks, the localization accuracy in relatively complex random motion is 0.1% to 1.69%.