A Spatio-Temporal Descriptor Based on 3D-Gradients

TLDR

The study introduces a novel local descriptor for video sequences based on histograms of oriented 3D spatio‑temporal gradients. The descriptor is computed using a memory‑efficient integral‑video algorithm for 3D gradients, a generic orientation quantization based on regular polyhedrons, and a thorough parameter optimization for action recognition. Applied to KTH, Weizmann, and Hollywood action datasets, the descriptor outperforms state‑of‑the‑art methods.

Abstract

In this work, we present a novel local descriptor for video sequences.The proposed descriptor is based on histograms of oriented 3D spatio-temporal gradients.Our contribution is four-fold. (i) To compute 3D gradients for arbitrary scales, we develop a memory-efficient algorithm based on integral videos. (ii) We propose a generic 3D orientation quantization which is based on regular polyhedrons. (iii) We perform an in-depth evaluation of all descriptor parameters and optimize them for action recognition. (iv) We apply our descriptor to various action datasets (KTH, Weizmann, Hollywood) and show that we outperform the state-of-the-art.