Combined curvelets and hidden Markov models for human fall detection

Abstract

Fall events detection is one of the most crucial issues in the health care of elderly people. This paper proposes an innovative approach for reliably detecting fall incidents based on human silhouette shape variation in vision monitoring. This mission is achieved by: (i) introducing the curvelet transform and area ratios for identifying human postures in images; (ii) reducing the feature vector dimension using differential evolution technique; (iii) identifying postures by a support vector machine, and (iv) adapting a hidden Markov model for classifying video sequences into non-fall and fall events. Experimental results are obtained on several "Fall Detection" datasets. For evaluation, several assessment measures are computed. These evaluation measures demonstrate the effectiveness of the proposed methodology when compared to some state-of-the-art approaches.