Human activity recognition based on ELM using depth Images
Human Activity Recognition (HAR) has gained considerable research interest in recent decades due to its vast applications especially in the fields of medicine, surveillance, human-machine interaction, gaming and entertainment. Feature extraction is a key step in HAR algorithms. However, at presen...
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| Format: | Thesis |
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| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78031/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78031/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78031/3/Ahmed%20Kawther.pdf |
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| Summary: | Human Activity Recognition (HAR) has gained considerable research interest in recent
decades due to its vast applications especially in the fields of medicine, surveillance,
human-machine interaction, gaming and entertainment. Feature extraction is a key step in
HAR algorithms. However, at present most research is focused on common features such as
spatial domain and frequency domain features. Such features lack context and are not
comprehensive in nature. Unfortunately, building a comprehensive feature space of human
activities is difficult due to the vastness and uncountable nature of human actions. This
leads to the challenging problem of designing a HAR system that uses context-based
feature extraction of human actions. In this work a comprehensive contextual feature space
for human activity recognition is presented using depth image,the total number of fratures
is 11. in classification aspect, extrem learning machine uses only a single iteration in the
training stage to determine the output weights. extrem learning machine is extremely
effective as it tends to achieve the global optimum compared to the traditional FNN
learning methods which might get trapped in a local optimum. The drawback of ELM
algorithm holds an infinite number of degrees of freedom for approximating a given data
set. These infinite degrees of freedom are a consequence of the random nature of the
weights assigned between the input and the hidden layer. A possible potential improvement
in performance in this research can be achieved by assigning the weights based on an
objective functionan optimization of the (ELM) using the meta-heuristic. Harmony Search
Algorithm which is a part of meta-heustric and Tansig activation function which remove un
needed hidden neuron are also presented in this work. The presented approach hence
solves the problem of the infinite degree of freedom of the input weights as well as
restricting the number of neurons in hidden layer, thus increasing the performance of the
ELM algorithm. The optimized ELM algorithm is then used to perform the classification of
the developed context based on feature space. The accuracy achieved was 100% during
training and 94.95% during testing with throw action and 100% during training and 100%
during testing without throw action. Gready optimization of the ELM with HSO has
acehived an accuracy of 94.95%. Moreover, 60% of the features have achieved an accuracy
of over 100%. Thus, the approach can be utilized to perform the human activity recognition
for various purposes. |
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