Statistical descriptors for human actions classification

The objective of this study is to investigate alternative ways for representing suitably, with the fewest possible assumptions, the information derived from video recordings. It proposes a set of statistical descriptors capable of summarizing all the available information from each video frame. A sequence of such features expresses the object motion implicitly without the need for object detection techniques and tedious pre-processing. A video application such as the human action recognition is then tackled as a time-series classification problem. Neural networks are used for the time-series learning; when they are simulated with a new human action video, their predictions constitute the input a typical classifier would require, in order for it to decide which model (from the known time-series) has possibly generated this video.