  In this paper, a novel human action recognition technique from video is
presented. Any action of human is a combination of several micro action
sequences performed by one or more body parts of the human. The proposed
approach uses spatio-temporal body parts movement (STBPM) features extracted
from foreground silhouette of the human objects. The newly proposed STBPM
feature estimates the movements of different body parts for any given time
segment to classify actions. We also proposed a rule based logic named rule
action classifier (RAC), which uses a series of condition action rules based on
prior knowledge and hence does not required training to classify any action.
Since we don't require training to classify actions, the proposed approach is
view independent. The experimental results on publicly available Wizeman and
MuHVAi datasets are compared with that of the related research work in terms of
accuracy in the human action detection, and proposed technique outperforms the
others.
