Online View-invariant Human Action Recognition Using RGB-D Spatio-temporal Matrix
Date Issued
2014
Date
2014
Author(s)
Hsu, Yen-Pin
Abstract
Understanding human action has drawn attention to the field of computer vision. We choose vision-based system so that computer system can understand human actions naturally. When people are recognizing actions of other people, the actors do not have to stand right in front of the observer. Therefore, in this thesis, we aim to build a vision-based action recognition system which is invariant to the viewpoint.
To achieve this goal, we include the idea of self-similarity. When two video sequences record a specific action from various camera views, the resulting appearances of actions would be entirely different. Consequently, if we simply apply feature extraction methods to the raw video, we will end up getting totally different features. Instead of doing the extraction of spatio-temporal feature for every frame and using these feature vectors directly, our study uses the Euclidean distance between feature vectors that are represented in a Self-Similarity Matrix (SSM). To recognize the action, we describe the local tendency of the SSM using pyramid-structural bag-of-words and train a Support-Vector Machine as our classifier. Extensive experiments have been conducted to validate the proposed action recognition system.
Subjects
動作辨識
無關視點
自身相似
Type
thesis
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