Appearance Based Robot and Human Activity Recognition System
This work addresses activity recognition for robots and humans in indoor environments, but it is incremental as it builds on existing methods like PCA and background modeling.
The authors tackled the problem of recognizing human and robot activities by developing an appearance-based system that uses background modeling and PCA for feature extraction, achieving efficacy in experiments on both robot-performed activities and standard human databases.
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.