Human activity recognition for video surveillance using sequences of postures
The Human activities recognition has become a research area of great interest as it has many potential applications; including automated surveillance, sign language interpretation and human-computer interfaces. In recent years, an extensive research has been conducted in this field. This paper...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English English |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/1/123.pdf http://irep.iium.edu.my/38609/6/search.pdf http://irep.iium.edu.my/38609/12/38609_Human%20activity%20recognition%20for%20video.SCOPUSpdf.pdf |
| Summary: | The Human activities recognition has become a
research area of great interest as it has many potential
applications; including automated surveillance, sign language
interpretation and human-computer interfaces. In recent years,
an extensive research has been conducted in this field. This paper
presents a part of a novel a Human posture recognition system
for video surveillance using one static camera. The training and
testing stages were implemented using four different classifiers
which are K Means, Fuzzy C Means, Multilayer Perceptron SelfOrganizing
Maps and Feedforward Neural networks. The
accuracy recognition of used classifiers is calculated. The results
indicate that Self-Organizing Maps shows the highest recognition
rate. Moreover, results show that supervised learning classifiers
tend to perform better than unsupervised classifiers for the case
of human posture recognition. Furthermore, for each individual
classifier, the recognition rate has been found to be proportional
to the number of training postures. Performance comparisons
between the proposed system and existing similar systems were
also shown. |
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