Gait recognition method based on dual-level temporal features
At present,most gait recognition methods focus on the modeling of a single time scale of gait sequences,ignoring the information interaction of different time scales.Based on this,a dual-scale temporal feature representation network is proposed.This method aggregates two time level features to obtain the motion representation of gait,and fuses the features on the two time scales to achieve information interaction.Through experimental verification,the performance of this method on the data set CASIA-B sur-passes the mainstream gait recognition method,and the Rank-1 accuracy rate reaches 97.8%,93.1%and 80.6%under NM,BG and CL conditions,respectively.
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NSTL
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