Daily activity recognition using the informative features from skeletal and depth data

Abstract

In this paper, we present an efficient framework for human activity recognition in daily environment. We use depth information mainly for privacy protection, and then focus on the motion analysis of informative body parts, since most activities are much associated with these particular parts, e.g., head and hands in upper body. Based on the idea, we propose two novel features with intuitive physical meaning, which are Histogram of Located Displacements (HOLD) and Local Depth Motion Maps (L-DMM) based Gabor representation. They can capture discriminative posture and motion cues from skeletal joints and depth data respectively. Combing the advantages of joint and depth features as well as emphasizing the reliable parts can enhance the robustness of classification ability. The experimental results show that our proposed feature representation is very discriminative for the task of daily activity recognition and outperforms several state-of-the-art methods.