A hierarchical bagging-SVM human behavior recognition method

Yulong Liang; Xiaochao Dang; Zhanjun Hao

doi:10.1117/12.2624860

13 December 2021 A hierarchical bagging-SVM human behavior recognition method

Yulong Liang, Xiaochao Dang, Zhanjun Hao

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 1208724 (2021) https://doi.org/10.1117/12.2624860
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Aiming at the problems of using mobile device sensors to recognize human behaviors such as low accuracy, few types, and no consideration of transition actions, this paper proposes a hierarchical Bagging-SVM human behavior recognition method. This method first applies the moving average filtering algorithm to the collected sensor data for noise reduction and smoothing; then calculates the amplitude of the acceleration vector sum to characterize human motion behavior, extracts time domain features to build a layered model; finally uses the Bagging-SVM algorithm Perform hierarchical recognition of behaviors in hierarchical models. Experimental results show that compared with other recognition methods, this method can recognize human behavior more accurately and has higher robustness.

Citation Download Citation

Yulong Liang, Xiaochao Dang, and Zhanjun Hao "A hierarchical bagging-SVM human behavior recognition method", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 1208724 (13 December 2021); https://doi.org/10.1117/12.2624860

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KEYWORDS

Sensors

Data modeling

Mobile devices

Detection and tracking algorithms

Calibration

Motion models

Feature extraction

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