A Novel Approach for Human Action Recognition from Silhouette Images
This work addresses action recognition for video analysis, offering a view-independent approach, but it is incremental as it builds on existing silhouette-based methods.
The paper tackles human action recognition from silhouette images by introducing a novel spatio-temporal body parts movement (STBPM) feature and a rule-based classifier (RAC) that eliminates the need for training, achieving improved accuracy on Wizeman and MuHVAi datasets compared to existing methods.
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.