The recognition of human movement using temporal templates

TLDR

The study proposes a view‑based representation and recognition method for human movement using temporal templates. The method represents motion as a temporal template—a static vector image encoding motion presence and recency—and matches these templates to stored view instances of known actions. The approach achieves automatic temporal segmentation, speed invariance, and real‑time performance on standard hardware.

Abstract

A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.