A compact sequence encoding scheme for online human activity recognition in HRI applications

TL;DR

This paper introduces a compact encoding scheme for online human activity recognition that transforms spatio-temporal sequences into efficient representations suitable for lightweight neural networks, enabling deployment on low-resource hardware.

Contribution

It proposes a novel sequence encoding method using Mahalanobis distance and Radon transform for robust, real-time activity recognition in resource-constrained environments.

Findings

Effective in online recognition scenarios

Compatible with low-resource hardware

Achieves robustness with pose estimation techniques

Abstract

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.