Skeleton-based STIP feature and discriminant sparse coding for human action recognition

Abstract

Purpose

To find a successful human action recognition system (HAR) for the unmanned environments.

Design/methodology/approach

This paper describes the key technology of an efficient HAR system. In this paper, the advancements for three key steps of the HAR system are presented to improve the accuracy of the existing HAR systems. The key steps are feature extraction, feature descriptor and action classification, which are implemented and analyzed. The usage of the implemented HAR system in the self-driving car is summarized. Finally, the results of the HAR system and other existing action recognition systems are compared.

Findings

This paper exhibits the proposed modification and improvements in the HAR system, namely the skeleton-based spatiotemporal interest points (STIP) feature and the improved discriminative sparse descriptor for the identified feature and the linear action classification.

Research limitations/implications

The experiments are carried out on captured benchmark data sets and need to be analyzed in a real-time environment.

Practical implications

The middleware support between the proposed HAR system and the self-driven car system provides several other challenging opportunities in research.

Social implications

The authors’ work provides the way to go a step ahead in machine vision especially in self-driving cars.

Originality/value

The method for extracting the new feature and constructing an improved discriminative sparse feature descriptor has been introduced.

Citation

P, U. and G G, L.P. (2021), "Skeleton-based STIP feature and discriminant sparse coding for human action recognition", International Journal of Intelligent Unmanned Systems, Vol. 9 No. 1, pp. 43-61. https://doi.org/10.1108/IJIUS-12-2019-0067