A New Effective Speed and Distance Feature Descriptor Based on Optical Flow Approach in HAR

Abstract

Nowadays, artificial intelligence and computer vision have been applied in various domains in the real world, such as Autonomous vehicles, video surveillance, human activity recognition, face recognition, smart home, and automated industry. Video-based human activity recognition is a big challenge yet. This paper proposes the Gaussian Mixture Model and Optical Flow approach to detect foreground and feature extraction for human activity recognition. The speed with a range of frames and radial distance from the Centroid to edge points of the human silhouette describe the feature vector of human activities. And then, the features are classified by multi-class SVM. The proposed system has been tested in the Weizmann datasets and KTH datasets. The experiment shows that our methods distinguish walking, bending, running, and wave-hand efficiently and accurately.