Appearance Based Robot and Human Activity Recognition System

TL;DR

This paper introduces an appearance-based system for recognizing human and robot activities using background modeling, feature extraction via PCA, and classification, validated through experiments with robots and standard databases.

Contribution

It presents a novel appearance-based activity recognition system that combines background modeling and PCA for feature extraction, applicable to both humans and robots.

Findings

Effective activity classification demonstrated on robot and human datasets

Low-dimensional features improve recognition accuracy

System performs well in indoor environments

Abstract

In this work, we present an appearance based human activity recognition system. It uses background modeling to segment the foreground object and extracts useful discriminative features for representing activities performed by humans and robots. Subspace based method like principal component analysis is used to extract low dimensional features from large voluminous activity images. These low dimensional features are then used to classify an activity. An apparatus is designed using a webcam, which watches a robot replicating a human fall under indoor environment. In this apparatus, a robot performs various activities (like walking, bending, moving arms) replicating humans, which also includes a sudden fall. Experimental results on robot performing various activities and standard human activity recognition databases show the efficacy of our proposed method.