Chord-Length Shape Features for Human Activity Recognition

Abstract EN

Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human action recognition literature.

In this paper, we present a new approach for human activity recognition, based on chord-length shape features.

The most interesting contribution of this paper is twofold.

We first show how a compact, computationally efficient shape descriptor; the chord-length shape features are constructed using 1-D chord-length functions.

Second, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states.

On two benchmark action datasets (KTH and WEIZMANN), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.