Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

TL;DR

This paper compares various action recognition methods, including Riemannian manifolds, Fisher vectors, and GMMs, under ideal and challenging conditions, highlighting the superior performance of Fisher vectors in most scenarios.

Contribution

It provides a comprehensive comparison of manifold-based and traditional action recognition techniques under controlled and challenging conditions.

Findings

Fisher vectors outperform manifold-based methods under ideal conditions.

Manifold techniques are less stable in challenging scenarios.

Fisher vectors handle moderate scale and translation variations effectively.

Abstract

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes.