Research on Trajectory Matching Method Based on the Fusion of Deep Learning and DTW
To address the problem of matching human trajectories with wearable sensor data,this paper proposes a solution that matches human trajectories captured by cameras with sensor data from wearable devices.First,the deep learning model SyncScoreDTW is used to evaluate the similarity between trajectories and sensor data over unit time.Next,a likelihood fusion algorithm is employed to progressively update these similarities.Experiments conducted on a self-made dataset demonstrate that this method achieves a high matching accuracy of 77.5%.Additionally,on the publicly available UEA dataset,the method also shows superior performance.This research reveals the potential of cross-modal data matching and provides a novel and efficient solution for the field of trajectory matching.
deep learningtime serieshuman trajectorysensorsmultimodal sensor data analysisTransformer
NETL
NSTL
万方数据
