A Novel Approach for Human Action Recognition from Silhouette Images

TL;DR

This paper introduces a training-free, view-independent human action recognition method using spatio-temporal body part movement features and a rule-based classifier, demonstrating superior accuracy on public datasets.

Contribution

It presents a novel, training-free approach combining spatio-temporal features with rule-based classification for human action recognition from silhouettes.

Findings

Outperforms existing methods in accuracy on Wizeman and MuHVAi datasets.

Does not require training, enabling view independence.

Effective in recognizing actions from silhouette images.

Abstract

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.