  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.

Robotics;Computer Vision and Pattern Recognition