Learner posture recognition via a fusing model based on improved SILTP and LDP

Abstract

The learner’s body posture reflects the learner’s learning state. The learner’s posture recognition can effectively evaluate their learning state, which plays an important role in the teacher’s teaching process. In this paper, a new method for learner’s gesture recognition is proposed, which fuses the improved scale invariant local ternary pattern (SILTP) and the local directional pattern (LDP). Firstly, a multi-scale weighted adaptive SILTP (MWA-SILTP) algorithm is proposed. The dynamic threshold of the current neighborhood is adaptively generated according to the dispersion degree of contrast values in global and local neighborhoods, and SILTP coding is carried out to obtain adaptive SILTP. And the concept of multi-scale is introduced. By changing the sampling radius, the adaptive SILTPs of different scales are obtained. The adaptive SILTPs of different scales are merged with different weights to represent the image in multi-resolution. The MWA-SILTP algorithm is used to extract the feature of the learner’s posture image. Secondly, the LDP algorithm is used to extract the feature of the learner’s posture image. Finally, the two features are merged, and the support vector machine is used for classification and recognition. The improved SILTP can get more feature information and has stronger adaptability. The LDP algorithm has advantages in anti-interference and can extract edge information better. The fusing model of this paper fully utilizes the advantages of the two algorithms. Experimental results show that the proposed method can effectively recognize learner’s posture of sitting, raising hand and lowering head.