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View-invariant action recognition based on Artificial Neural Networks


IEEE Projects Years : 2012

View-invariant action recognition based on Artificial Neural Networks

In this paper, a novel view invariant action recognition method based on
neural network representation and recognition is proposed. The project has
employed the technique mentioned and excellent results were obtained for a
number of widely used font types. The technical approach followed in processing
input images, detecting graphic symbols, analyzing and mapping the symbols and
training the network for a set of desired Unicode characters corresponding to the
input images are discussed in the subsequent sections. Even though the
implementation might have some limitations in terms of functionality and
robustness, the researcher is confident that it fully serves the purpose of addressing
the desired objectives. The novel representation of action images is based on
learning spatially related prototypes using Self Organizing Maps (SOM). Fuzzy
distances from prototypes are used to produce a time invariant action
representation. Multilayer perceptions’ are used for action classification. The
algorithm is trained using data from a various setup. An arbitrary number of
images can be used in order to recognize actions using a Bayesian framework. The
proposed method can also be applied to the depicting interactions between images,
without any modification. The use of information captured from different viewing
angles leads to high classification performance. The proposed method is the first
one that has been tested in challenging experimental setups, a fact that denotes its
effectiveness to deal with most of the open issues in action recognition.
IEEE 2012 Transactions on Neural Networks and Learning Systems, Volume: 23 , Issue: 3
Existing System:
View-invariant action recognition method it is not Support
previous..Doesn’t implement the MLP it is mainly to analyzing the angles.
Proposed System:
Inspired from this setting, novel approach in view independent
action recognition is proposed. Trying to solve the generic action recognition
problem, a novel view-invariant action recognition method based on ANNs is
proposed in this paper. Action recognition results are subsequently combined to
recognize the unknown action. The proposed method performs view independent
action recognition; second MLP is proposed to identify the viewing angle. An
emerging technique in this particular application area is the use of Artificial Neural
Network implementations with networks employing specific guides (learning
rules) to update the links (weights) between their nodes. Such networks can be fed
the data from the graphic analysis of the input picture and trained to output
characters in one or another form. Specifically some network models use a set of
desired outputs to compare with the output and compute an error to make use of in
adjusting their weights. Such learning rules are termed as Supervised Learning.
This experiment illustrates the ability of the proposed approach to
recognize actions at high accuracy.
1. Artificial Neural Networks
2. The Multi-Layer Perceptron Neural Network Model
3. Optical Language Symbols
4. Region Maker for test region
1. Artificial Neural Networks:
Modeling systems and functions using neural network mechanisms is a
relatively new and developing science in computer technologies. The particular
area derives its basis from the way neurons interact and function in the natural
animal brain, especially humans. The animal brain is known to operate in
massively parallel manner in recognition, reasoning, reaction and damage
recovery. All these seemingly sophisticated undertakings are now understood to be
attributed to aggregations of very simple algorithms of pattern storage and
retrieval. Neurons in the brain communicate with one another across special
electrochemical links known as synapses. At a time one neuron can be linked to as
many as 10,000 others although links as high as hundred thousands are observed to
exist. The typical human brain at birth is estimated to house one hundred billion
plus neurons. Such a combination would yield a synaptic connection of 1015, which
gives the brain its power in complex spatio-graphical computation.
2. The Multi-Layer Perceptron Neural Network Model:
· It receives a number of inputs (either from original data, or from the output
of other neurons in the neural network). Each input comes via a connection
that has a strength (or weight); these weights correspond to synaptic efficacy
in a biological neuron. Each neuron also has a single threshold value. The
weighted sum of the inputs is formed, and the threshold subtracted, to
compose the activation of the neuron (also known as the post-synaptic
potential, or PSP, of the neuron).
· The activation signal is passed through an activation function (also known as
a transfer function) to produce the output of the neuron.
3. Optical Language Symbols:
Several languages are characterized by having their own
written symbolic representations (characters). These characters are either a
delegate of a specific region, accent or whole words in some cases. In terms of
structure world language characters manifest various levels of organization. With
respect to this structure there always is an issue of compromise between ease of
construction and space conservation. Highly structured alphabets like the Latin set
enable easy construction of language elements while forcing the use of additional
space. Medium structure alphabets like the Ethiopic (Ge’ez) conserve space due to
representation of whole audioglyphs and tones in one symbol, but dictate the
necessity of having extended sets of symbols and thus a difficult level of use and
learning. Some alphabets, namely the oriental alphabets, exhibit a very low amount
of structuring that whole words are delegated by single symbols. Such languages
are composed of several thousand symbols and are known to need a learning cycle
spanning whole lifetimes. ANSI and named the ASCII Character set. It is
composed of and 8-bit encoded computer symbols with a total of 256 possible
unique symbols.
4. Region Maker for test region:
After making the image from neural network, using
region maker we have to cut the image as we want and note the cutting image
Action Recognition Algorithm
By using a 13×13 SOM an action recognition rate equal to
89:8% has been obtained. Table V illustrates comparison results with three
methods evaluating their performance in the IXMAS multi-view action recognition
database. As can be seen, the proposed method outperforms these methods
providing up to 8:5% improvement on the action classification accuracy.
Hardware Required:
System : Pentium IV 2.4 GHz
Hard Disk : 40 GB
Floppy Drive : 1.44 MB
Monitor : 15 VGA color
Mouse : Logitech.
Keyboard : 110 keys enhanced
RAM : 256 MB
Software Required:
O/S : Windows XP.
Language : c#.Net
Data Base : Sql Server 2005.






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