Silhouette-based human action recognition with a multi-class support vector machine
Keywords: Bag of key poses, MuHAVi, SVM, Computer vision, Human Action Recognition
Computer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognized, we take a multiclass classification approach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognizing actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively.
|Fecha de publicación:||2018|
|Año de Inicio/Término:||22-24 May 2018|
|Página de inicio:||80|