Abstract
This study presents a novel systemfor human action recognition. Two research issues, namely, motion representation and subspace learning, are addressed. In order to have a rich motion descriptor, we propose to combine the distance signal and the width feature so that a silhouette can be characterized in more detail. These two features provide complementary information and are integrated to yield a better discriminative power. The combined features are subsequently quantized into mid-level features using k-means clustering. In the mid-level feature space, we apply the Nonparametric Weighted Feature Extraction (NWFE) to construct a compact yet discriminative subspace model. Finally, we can simply train a Bayes classifier for recognizing human actions. We have conducted a series of experiments on two publicly available datasets to demonstrate the effectiveness of the proposed system. Compared with the existing approaches, our system has a significantly reduced complexity in classification stage whilemaintaining high accuracy.
Highlights
Recognizing human actions from video sequences is an important area of research in computer vision
We propose a novel method based on the Nonparametric Weighted Feature Extraction (NWFE) [10] to tackle this problem
The system first performs a combined feature, which integrates the signal distance feature and the width feature extracted from a human pose silhouette
Summary
Recognizing human actions from video sequences is an important area of research in computer vision. This technology has many practical applications such as video surveillance [1,2,3], human-computer interaction [4], entertainment [5], sports video analysis [6], and smart rooms [7]. To resolve the above-mentioned issues, one needs a reliable representation that can deal with spatial-temporal scaling variations associated with human actions. The chosen representation must encapsulate the unique characteristics of an action performed by different persons. Hidden Markov Model (HMM) is arguably the most popular approach for modelling and classifying human actions. One usually does not have sufficient training data for learning the model parameters
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