Harris corner detector and blob analysis featuers in human activty recognetion
the automated detection and monitoring of human activities have gained increased attention in the last decade due to many video applications. They are playing a central role of behavior analysis of human being, where adequate monitoring can minimize the risk of harm to our society. Although, the...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/62660/ http://irep.iium.edu.my/62660/ http://irep.iium.edu.my/62660/ http://irep.iium.edu.my/62660/1/62660_Harris%20corner%20detector%20and%20blob%20analysis%20featuers.pdf http://irep.iium.edu.my/62660/7/62660_Harris%20corner%20detector%20and%20blob%20analysis%20featuers_scopus.pdf |
| Summary: | the automated detection and monitoring of human
activities have gained increased attention in the last decade due to
many video applications. They are playing a central role of
behavior analysis of human being, where adequate monitoring
can minimize the risk of harm to our society. Although, the
activities recognition has been studied by many researchers but it
still inaccurate. This because of high similarity between human
joints when its move to perform some activities such as walking,
running and jogging. In this paper, a human activity recognition
system was designed based on features extraction analysis. Two
types of features extractions techniques were used, which are the
basic blob analysis features and Harris corner detector. By
comparing the accuracy of the recognition rate in each technique
through the two scenarios we found that Harris corner detector is
more powerful than the basic blob analysis features because of it
is capable to distinguish between the similar activities in an
accurate manner |
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