A Reliable-Inference Framework for Recognition of Human Actions

We present an action recognition method based on the concept of reliable inference. Our approach is formulated in a probabilistic framework using posterior class ratios to verify the saliency of an input before committing to any action classification. The framework is evaluated in the context of walking, running, and standing at multiple views and compared to ML and MAP approaches. Results examining individual silhouette images with the framework demonstrate that these actions can be reliably discriminated while discounting confusing images.