A survey about view-invariant human action recognition
Human action recognition has been extensively studied with a lot of real life application. Many methods have been proposed and achieved promising results when the input video captured from the same viewpoints. However, their accuracy decreases significantly under viewpoint changing. The reason is that action appearance is quite different when looking from a different angle. To overcome this problem, many researchers have shifted their focus on how to develop a robust action recognition method where training and testing are carried on a different viewpoint. The aim of this paper is to give a systematic review of current methods for view-invariant action recognition and how they overcome the viewpoint change. Moreover, this paper also introduces multi-view action recognition datasets which are widely used for testing the view-invariant ability. Benchmarks of various techniques on these datasets are also provided to show the current state of research.