Multiple Viewpoints Action Recognition Based on Height and Shape Information
Date Issued
2012
Date
2012
Author(s)
Sun, Pei-Chiang
Abstract
Researches for human action recognition system become more and more popular in recent years since people want to make machine more interactive and friendly. To increase the applicability of the action recognition system, the system should recognize human actions under view changes. Thus, this thesis proposes a view-invariant action recognition system which can recognize human actions under view changes.
For the action recognition mechanism, the proposed system uses a sequence of postures to infer human actions. Usually, human can perceive actions by observing only the human body postures. Inspired by this property, the relativity between a posture and various actions can be represented as a vector which is called weighting vector. Then, the propose system can infer human actions based on the weighting vectors of postures.
However, because of the viewing angle effect of a single camera, the system cannot recognize the human posture correctly under view changes by only using shape information; inversely, the height distribution of human body would not vary under view changes. Namely, the height distribution of human body is another significant feature for posture recognition. Thus, in this thesis, a RGB-Depth camera is used to extract the shape and the height information from human body for posture recognition to improve the performance of the proposed system.
The proposed system has been tested by sixteen kinds of actions under view changes and achieve 96.9% recognition rate.
Subjects
Action recognition
multiple viewpoints
key posture
height distribution of human body
depth map
Type
thesis
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