Adaptive Lp-Norm Regularized Sparse Representation for Human Activity Recognition in Coal Mines

Abstract

This paper aims to overcome the lack of in-depth exploration into the intrinsic geometry of human activities. For this purpose, a generalized adaptive Lp-norm regularized sparse representation (ARSR) approach was proposed for human activity recognition, which preserves the model adaptability through the adaptive Lp-norm regularization. In essence, the proposed method applies sparse representation to human activity recognition, turning it into a new optimization problem. In addition, the problem was solved by the iterative-shrinkage-thresholding algorithm. Specifically, the sparse representation learned by the ARSR algorithm was introduced into the support vector machine (SVM) classifier. Then, several experiments were conducted on coal-mining datasets for human activity identification. The experimental results revealed that the proposed algorithm is superior to the current sparse representation algorithms like the standard L1-norm regularized sparse representation algorithm. The research findings shed new light on the human activity recognition in coal mines.