Minimal-latency human action recognition using reliable-inference

We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information. q 2006 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2006
• Where: IVC
• Authors: James W. Davis, Ambrish Tyagi
• Keywords: Action Classes, Human Actions, IVC 2006, Probabilistic Reliable-inference Framework