Motion based action recognition using k nearest neighbor
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The document presents a motion-based approach for action recognition in videos, primarily using optical flow and k-nearest neighbor (KNN) methods. The proposed system involves three main stages: blob extraction, feature extraction, and action recognition, demonstrating 100% accuracy on both the KTH and Weizmann datasets. This research provides significant advancements in computer vision applications, including surveillance and performance analysis.
![IJRET: International Journal of Research in Engineering and Technology
__________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @
MOTION BASED ACTION RECOGNITION USING
Shikha
1
M.E. Student, Branch Digital Electronics, Department of Electronics and Tele
Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India.
2
Associate Professor, Branch Digital E
Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State,
Analyzing the actions of a person from a video by
topicin the area of computer vision. There
monitoring systems, human performance analysis, content
efficient applications are available of action recognition, the most active application domain in
is to “look at people”. In this paper, motion
human body and hence human actions can be effectively
shape. In the motion-based approach, the method that extracts
filtering or watershed transform are used for recognizing
analyzes human movements directly from
extraction and action recognition. In the first step
motion feature is extracted using optical flow
Keywords: Action Recognition, 2Dblob,
-----------------------------------------------------------------
1. INTRODUCTION
Recognizing or understanding the actions
from a video is the objective of action recognition.
The main objective of our method is to improve the
accuracy of recognition. Action recognition is classified in
four types: Object-level, Tracking-level, Pose
Activity-level. Object-level recognize the locations
object, Tracking-level recognize the object trajectories,
Pose-level recognize the pose of a person & Activity
recognize the activity of person.The major problems faced
in action recognition are: view-point variation
camera, temporal variation: Variation in duration and shift
spatial variation: Different people perform the
in different ways.n our paper we present a
eliminates all the above problems by using the concept of
optical flow and kNN.
2. TOOL
The tool used for recognition is MATLAB
7.10.0(R2010a)
3. DATASETS
The datasets used for Action Recognition are KTH and
Weizmann. [9], [12]
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319
____________________________________________________________________________
, Available @ http://www.ijret.org
MOTION BASED ACTION RECOGNITION USING
NEIGHBOR
Shikha.A.Biswas1
, Vasant.N.Bhonge2
M.E. Student, Branch Digital Electronics, Department of Electronics and Tele-communication, Shri Sant Gajanan
Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India.
Associate Professor, Branch Digital Electronics, Department of Electronics and Tele-
Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State,
India.
Abstract
nalyzing the actions of a person from a video by using computer is termed as Action Recognition.
are many applications of this research which include surveillance systems, patient
an performance analysis, content-based image/video retrieval/storage, virtual reality.
action recognition, the most active application domain in
motion feature is extracted because motion features can portray
can be effectively recognized by motion rather than other features
method that extracts motion of the human action such as
used for recognizing action. This paper presents a novel method of action recognition
n movements directly from video. The overall system consists of major three steps: blob extraction, feature
the first step, the input video is preprocessed to extract the 2D
feature is extracted using optical flow and at last action is recognized using classifier k-Nearest Neighbor (kNN
2Dblob, Optical Flow, kNN
-------------------------------***----------------------------------------------------------
actions of a person
from a video is the objective of action recognition.
The main objective of our method is to improve the
is classified into
level, Pose-level &
level recognize the locations of
trajectories,
person & Activity-level
.The major problems faced
point variation: movement of
Variation in duration and shift,
perform the same action
n our paper we present a method that
eliminates all the above problems by using the concept of
is MATLAB, version
The datasets used for Action Recognition are KTH and
Weizmann
It contains 10 types of actions performed by 9 subjects.
Thus it contains total 90 AVI videos, taken with a static
camera and static background and with a frame rate of 25
fps. Actions in this dataset include: bend, jack, run, side,
skip, wave1, wave2, jump, P
KTH
It contains 6 types of actions performed by 25 subjects in 4
homogenous backgrounds. Thus it contains total 600 AVI
videos, taken with a static camera over homogenous
backgrounds & with a frame rate of 25 fps. Actions in
dataset include: walking, jogging, running, boxing, hand
waving, and hand clapping.
4. METHOD
The proposed method contains
recognition:
Fig-1: Stages of Action Reco
Blob
Extraction
Feature
Extraction
eISSN: 2319-1163 | pISSN: 2321-7308
_______________________
587
MOTION BASED ACTION RECOGNITION USING k-NEAREST
communication, Shri Sant Gajanan
Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India.
-communication, Shri Sant
Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State,
Recognition. This is an active research
are many applications of this research which include surveillance systems, patient
, virtual reality. Although many
action recognition, the most active application domain in the area of computer vision
can portray the moving direction of
features such as color, texture or
such as motion blob, optical flow, FIR-
novel method of action recognition that
overall system consists of major three steps: blob extraction, feature
extract the 2D blob. In the second step,
Nearest Neighbor (kNN).
----------------------------------------------------------
It contains 10 types of actions performed by 9 subjects.
Thus it contains total 90 AVI videos, taken with a static
camera and static background and with a frame rate of 25
fps. Actions in this dataset include: bend, jack, run, side,
ave2, jump, P-jump walk.
It contains 6 types of actions performed by 25 subjects in 4
homogenous backgrounds. Thus it contains total 600 AVI
videos, taken with a static camera over homogenous
backgrounds & with a frame rate of 25 fps. Actions in this
walking, jogging, running, boxing, hand
proposed method contains three main stages of
of Action Recognition
Extraction
Action
Recognition](https://image.slidesharecdn.com/motionbasedactionrecognitionusingk-nearestneighbor-140811021505-phpapp01/85/Motion-based-action-recognition-using-k-nearest-neighbor-1-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 588
4.1 Blob Extraction
The most commonly used low-level feature for identifying
human action is 2D blob. Hence the first stage is called blob
extraction or segmentation or pre-processing stage
In this stage, the color video is first converted from RGB to
gray and then finally to binary. To remove the salt and
pepper noise, the gray scale video is first median filtered
and then is converted into binary using autothresholding.
Thus this stage divide the input video into two classes i.e.
foreground (activity of the human) called the 2D blob, and
background (empty frame). Then for enhancement, dilation
is done. In the dilation process, the binary video is dilated
by a structuring element (called mask or window) of size 3 x
3, 5 x 5 or 7 x 7.The structuring element of the proposed
method is shown below:
0 0 1 0 0
0 0 1 0 0
1 1 1 1 1
0 0 1 0 0
0 0 1 0 0
After dilation enhanced 2D blob is obtained. For fast and
extraction of the ‘motion’ feature, enhancement is done. [2],
[5], [11]easy
The blob extraction is shown below:
Input frame dilated frame
Fig-2: Blob extraction
4.2 Feature Extraction
After segmentation process, the next stage is feature
extraction. In this stage mid-level feature ‘motion’ is
extracted from the blob. Since the human action can be
effectively characterized by motion rather than other
features such as color, texture or shape, ‘motion’ feature is
extracted from the blob.We use optical flow to estimate
motion. Optical flow estimates the direction and speed of
moving object from one video frame to another. There are
two methods to find the Optical flow Horn-Schunck or
Lucas-Kanade method. For floating point input Horn-
Schunck method is used and for fixed point input Lucas-
Kanade method is used. We use Lucas-Kanade method to
find the optical flow. [1], [7], [8]
The optical flow of input video frame is shown below:
Input frame dilated frame optical flow
Fig-3: Feature Extraction
4.3 Action Recognition
This is the last stage of action recognition. For recognition,
we use k Nearest Neighbor (kNN). [1], [3], [4], [6]
kNN is a standard classifier which is mostly used for
action recognition because it does not require any learning
process and also it is invariant against view-point, spatial
and temporal variations.
Before classification using kNN, the proposed method
computes the following steps:
i) First the centroid of the connected region in the optical
flow is computed. This is called Blob analysis.
ii) The dimension of Blob analysis is very large; hence to
reduce its dimension Principal Component Analysis (PCA)
is done. PCA is a technique which reduces the dimension of
large data sets.
iii) Then covariance matrix of PCA is found.
iv) Then eigenvalues (EVA) of the covariance matrix are
found.EVA measure the magnitude of the corresponding
relative motion. This is called the training data for
classification.
v) Then the nearest neighbor in the training data is searched
using the distance metric ‘Euclidean distance’ of kNN.
vi) After k-Nearest Neighbor (kNN) search, they are
classified using kNN classifier. If the 1-NN is obtained for
each action in the dataset then it means that the action is
recognized.
vii) The result is also plotted using function ‘gscatter’ to
observe the classification.](https://image.slidesharecdn.com/motionbasedactionrecognitionusingk-nearestneighbor-140811021505-phpapp01/85/Motion-based-action-recognition-using-k-nearest-neighbor-2-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 590
7 DISCUSSION
By using the proposed method the accuracy of recognition
are shown below:
Table 1.Recognition using KTH Dataset
Type of
sequence
Total
seq
Correctly
Recognized
In %
walking 10 10 100
running 10 100
hand waving 10 10 100
handclapping 10 10 100
Boxing 10 10 100
jogging 10 10 100
Σ = 60 Σ = 60 Avg =100
Average % of accuracy using KTH dataset is 100%.
Table 2.Recognition using Weizmann Dataset
Type of
sequence
Total seq Correctly
Recognized
In %
walk 9 9 100
run 9 9 100
jack 9 9 100
skip 9 9 100
side 9 9 100
bend 9 9 100
jump 9 9 100
pjump 9 9 100
wave 1 9 9 100
wave 2 9 9 100
Σ = 90 Σ = 90 Avg =100
Average % of accuracy using Weizmann dataset is 100%.
8. CONCLUSION
This paper has presented a motion-based approach for action
recognition. It has used 2D blob as low-level feature and
extracts mid-level feature ‘motion’ from the blob using the
method Lucas Kanade of optical flow. The motion features
so obtained are classified using kNN classifier. The
advantage of using kNN is that it does not require any
learning process and also it is invariant against view-point,
temporal and spatial variations; hence its accuracy is good.
The average % of accuracy using the proposed method is
100% on Weizmann and KTH datasets.
REFERENCES
[1] S. Hari Kumar, P.Sivaprakash, “New Approach for
Action Recognition Using Motion based Features”,
Proceedings of 2013 IEEE Conference on Information and
Communication Technologies (ICT 2013), pp.1247-1252.
[2] Hetal Shah, N. C. Chauhan, “Recognition of Human
Actions in Video”, International Journal on Recent and
Innovation Trends in Computing and Communication
(IJRITCC) May 2013, ISSN 2321 -8169Volume 1,Issue 5,
pp.489 – 493.
[3] Xiaodong Yang and YingLi Tian, “Eigen Joints-based
Action Recognition Using Naïve-Bayes-Nearest-Neighbor”,
2012 IEEE, pp.14-19.
[4] Mi Zhang Alexander A. Sawchuk, “Motion Primitive-
Based Human Activity Recognition Using a Bag-of-
Features Approach”, IHI’12, January 28–30, 2012, Miami,
Florida, USA.
[5] Muhammad Hameed Siddiqi, Muhammad Fahim,
Sungyoung Lee, Young-Koo Lee, “ Human Activity
Recognition Based on Morphological Dilation followed by
Watershed Transformation Method ”, 2010 International
Conference on Electronics and Information Engineering
(ICEIE 2010),Volume 2, 2010 IEEE,V2 433-V2 437.
[6] Ronald Poppe, “A survey on vision-based human action
recognition”, Science Direct Image and Vision Computing
28 (2010) 976–990.
[7] Mohiuddin Ahmad, Seong-Whan Lee, “Human action
recognition using shape and CLG-(Combined local-global)
motion flow from multi-view image sequences”, Science
Direct Pattern Recognition 41 (2008), 2237 – 2252.
[8] Mohiuddin Ahmad and Seong-Whan Lee, “HMM-based
Human Action Recognition Using Multiview Image
Sequences”, Proceedings of the 18th International
Conference on Pattern Recognition (ICPR'06), 2006 IEEE.
[9] Moshe Blank, Lena Gorelick, Eli Shechtman, Michal
Irani, Ronen Basri, “ Actions as space–time shapes”,
Proceedings of the International Conference On Computer
Vision (ICCV’05), vol. 2, Beijing, China, October 2005, pp.
1395– 1402.
[10] O. Masoud and N. Papanikolopoulos, “A method for
human action recognition,” IVC, Vol. 21, 2003, pp.729-743.
[11] J. K. Aggarwal and Q. Cai, “Human Motion Analysis:
A Review”, idealibrary: Computer Vision and Image
Understanding,Vol. 73, No. 3, March 1999, pp. 428–440.
[12] http://www.nada.kth.se/cvap/actions](https://image.slidesharecdn.com/motionbasedactionrecognitionusingk-nearestneighbor-140811021505-phpapp01/85/Motion-based-action-recognition-using-k-nearest-neighbor-4-320.jpg)
Motion based action recognition using k nearest neighbor
- 1. IJRET: International Journal of Research in Engineering and Technology __________________________________________________________ Volume: 03 Issue: 01 | Jan-2014, Available @ MOTION BASED ACTION RECOGNITION USING Shikha 1 M.E. Student, Branch Digital Electronics, Department of Electronics and Tele Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India. 2 Associate Professor, Branch Digital E Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State, Analyzing the actions of a person from a video by topicin the area of computer vision. There monitoring systems, human performance analysis, content efficient applications are available of action recognition, the most active application domain in is to “look at people”. In this paper, motion human body and hence human actions can be effectively shape. In the motion-based approach, the method that extracts filtering or watershed transform are used for recognizing analyzes human movements directly from extraction and action recognition. In the first step motion feature is extracted using optical flow Keywords: Action Recognition, 2Dblob, ----------------------------------------------------------------- 1. INTRODUCTION Recognizing or understanding the actions from a video is the objective of action recognition. The main objective of our method is to improve the accuracy of recognition. Action recognition is classified in four types: Object-level, Tracking-level, Pose Activity-level. Object-level recognize the locations object, Tracking-level recognize the object trajectories, Pose-level recognize the pose of a person & Activity recognize the activity of person.The major problems faced in action recognition are: view-point variation camera, temporal variation: Variation in duration and shift spatial variation: Different people perform the in different ways.n our paper we present a eliminates all the above problems by using the concept of optical flow and kNN. 2. TOOL The tool used for recognition is MATLAB 7.10.0(R2010a) 3. DATASETS The datasets used for Action Recognition are KTH and Weizmann. [9], [12] IJRET: International Journal of Research in Engineering and Technology eISSN: 2319 ____________________________________________________________________________ , Available @ http://www.ijret.org MOTION BASED ACTION RECOGNITION USING NEIGHBOR Shikha.A.Biswas1 , Vasant.N.Bhonge2 M.E. Student, Branch Digital Electronics, Department of Electronics and Tele-communication, Shri Sant Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India. Associate Professor, Branch Digital Electronics, Department of Electronics and Tele- Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State, India. Abstract nalyzing the actions of a person from a video by using computer is termed as Action Recognition. are many applications of this research which include surveillance systems, patient an performance analysis, content-based image/video retrieval/storage, virtual reality. action recognition, the most active application domain in motion feature is extracted because motion features can portray can be effectively recognized by motion rather than other features method that extracts motion of the human action such as used for recognizing action. This paper presents a novel method of action recognition n movements directly from video. The overall system consists of major three steps: blob extraction, feature the first step, the input video is preprocessed to extract the 2D feature is extracted using optical flow and at last action is recognized using classifier k-Nearest Neighbor (kNN 2Dblob, Optical Flow, kNN -------------------------------***---------------------------------------------------------- actions of a person from a video is the objective of action recognition. The main objective of our method is to improve the is classified into level, Pose-level & level recognize the locations of trajectories, person & Activity-level .The major problems faced point variation: movement of Variation in duration and shift, perform the same action n our paper we present a method that eliminates all the above problems by using the concept of is MATLAB, version The datasets used for Action Recognition are KTH and Weizmann It contains 10 types of actions performed by 9 subjects. Thus it contains total 90 AVI videos, taken with a static camera and static background and with a frame rate of 25 fps. Actions in this dataset include: bend, jack, run, side, skip, wave1, wave2, jump, P KTH It contains 6 types of actions performed by 25 subjects in 4 homogenous backgrounds. Thus it contains total 600 AVI videos, taken with a static camera over homogenous backgrounds & with a frame rate of 25 fps. Actions in dataset include: walking, jogging, running, boxing, hand waving, and hand clapping. 4. METHOD The proposed method contains recognition: Fig-1: Stages of Action Reco Blob Extraction Feature Extraction eISSN: 2319-1163 | pISSN: 2321-7308 _______________________ 587 MOTION BASED ACTION RECOGNITION USING k-NEAREST communication, Shri Sant Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University, Maharashtra State, India. -communication, Shri Sant Gajanan Maharaja College of Engineering, Shegaon, Sant Gadge Baba Amravati University ,Maharashtra State, Recognition. This is an active research are many applications of this research which include surveillance systems, patient , virtual reality. Although many action recognition, the most active application domain in the area of computer vision can portray the moving direction of features such as color, texture or such as motion blob, optical flow, FIR- novel method of action recognition that overall system consists of major three steps: blob extraction, feature extract the 2D blob. In the second step, Nearest Neighbor (kNN). ---------------------------------------------------------- It contains 10 types of actions performed by 9 subjects. Thus it contains total 90 AVI videos, taken with a static camera and static background and with a frame rate of 25 fps. Actions in this dataset include: bend, jack, run, side, ave2, jump, P-jump walk. It contains 6 types of actions performed by 25 subjects in 4 homogenous backgrounds. Thus it contains total 600 AVI videos, taken with a static camera over homogenous backgrounds & with a frame rate of 25 fps. Actions in this walking, jogging, running, boxing, hand proposed method contains three main stages of of Action Recognition Extraction Action Recognition
- 2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 588 4.1 Blob Extraction The most commonly used low-level feature for identifying human action is 2D blob. Hence the first stage is called blob extraction or segmentation or pre-processing stage In this stage, the color video is first converted from RGB to gray and then finally to binary. To remove the salt and pepper noise, the gray scale video is first median filtered and then is converted into binary using autothresholding. Thus this stage divide the input video into two classes i.e. foreground (activity of the human) called the 2D blob, and background (empty frame). Then for enhancement, dilation is done. In the dilation process, the binary video is dilated by a structuring element (called mask or window) of size 3 x 3, 5 x 5 or 7 x 7.The structuring element of the proposed method is shown below: 0 0 1 0 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 1 0 0 After dilation enhanced 2D blob is obtained. For fast and extraction of the ‘motion’ feature, enhancement is done. [2], [5], [11]easy The blob extraction is shown below: Input frame dilated frame Fig-2: Blob extraction 4.2 Feature Extraction After segmentation process, the next stage is feature extraction. In this stage mid-level feature ‘motion’ is extracted from the blob. Since the human action can be effectively characterized by motion rather than other features such as color, texture or shape, ‘motion’ feature is extracted from the blob.We use optical flow to estimate motion. Optical flow estimates the direction and speed of moving object from one video frame to another. There are two methods to find the Optical flow Horn-Schunck or Lucas-Kanade method. For floating point input Horn- Schunck method is used and for fixed point input Lucas- Kanade method is used. We use Lucas-Kanade method to find the optical flow. [1], [7], [8] The optical flow of input video frame is shown below: Input frame dilated frame optical flow Fig-3: Feature Extraction 4.3 Action Recognition This is the last stage of action recognition. For recognition, we use k Nearest Neighbor (kNN). [1], [3], [4], [6] kNN is a standard classifier which is mostly used for action recognition because it does not require any learning process and also it is invariant against view-point, spatial and temporal variations. Before classification using kNN, the proposed method computes the following steps: i) First the centroid of the connected region in the optical flow is computed. This is called Blob analysis. ii) The dimension of Blob analysis is very large; hence to reduce its dimension Principal Component Analysis (PCA) is done. PCA is a technique which reduces the dimension of large data sets. iii) Then covariance matrix of PCA is found. iv) Then eigenvalues (EVA) of the covariance matrix are found.EVA measure the magnitude of the corresponding relative motion. This is called the training data for classification. v) Then the nearest neighbor in the training data is searched using the distance metric ‘Euclidean distance’ of kNN. vi) After k-Nearest Neighbor (kNN) search, they are classified using kNN classifier. If the 1-NN is obtained for each action in the dataset then it means that the action is recognized. vii) The result is also plotted using function ‘gscatter’ to observe the classification.
- 3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 589 5. FLOWCHART OF THE PROPOSED METHOD 6. RESULTS Input video Dilated frame Optical flow Recognized output Fig – 4: Recognition Result Input Video Blob Extraction Optical Flow Blob Analysis PCA Find the covariance matrix Find its Eigen values kNN search kNN classify Recognized output
- 4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 590 7 DISCUSSION By using the proposed method the accuracy of recognition are shown below: Table 1.Recognition using KTH Dataset Type of sequence Total seq Correctly Recognized In % walking 10 10 100 running 10 100 hand waving 10 10 100 handclapping 10 10 100 Boxing 10 10 100 jogging 10 10 100 Σ = 60 Σ = 60 Avg =100 Average % of accuracy using KTH dataset is 100%. Table 2.Recognition using Weizmann Dataset Type of sequence Total seq Correctly Recognized In % walk 9 9 100 run 9 9 100 jack 9 9 100 skip 9 9 100 side 9 9 100 bend 9 9 100 jump 9 9 100 pjump 9 9 100 wave 1 9 9 100 wave 2 9 9 100 Σ = 90 Σ = 90 Avg =100 Average % of accuracy using Weizmann dataset is 100%. 8. CONCLUSION This paper has presented a motion-based approach for action recognition. It has used 2D blob as low-level feature and extracts mid-level feature ‘motion’ from the blob using the method Lucas Kanade of optical flow. The motion features so obtained are classified using kNN classifier. The advantage of using kNN is that it does not require any learning process and also it is invariant against view-point, temporal and spatial variations; hence its accuracy is good. The average % of accuracy using the proposed method is 100% on Weizmann and KTH datasets. REFERENCES [1] S. Hari Kumar, P.Sivaprakash, “New Approach for Action Recognition Using Motion based Features”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), pp.1247-1252. [2] Hetal Shah, N. C. Chauhan, “Recognition of Human Actions in Video”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC) May 2013, ISSN 2321 -8169Volume 1,Issue 5, pp.489 – 493. [3] Xiaodong Yang and YingLi Tian, “Eigen Joints-based Action Recognition Using Naïve-Bayes-Nearest-Neighbor”, 2012 IEEE, pp.14-19. [4] Mi Zhang Alexander A. Sawchuk, “Motion Primitive- Based Human Activity Recognition Using a Bag-of- Features Approach”, IHI’12, January 28–30, 2012, Miami, Florida, USA. [5] Muhammad Hameed Siddiqi, Muhammad Fahim, Sungyoung Lee, Young-Koo Lee, “ Human Activity Recognition Based on Morphological Dilation followed by Watershed Transformation Method ”, 2010 International Conference on Electronics and Information Engineering (ICEIE 2010),Volume 2, 2010 IEEE,V2 433-V2 437. [6] Ronald Poppe, “A survey on vision-based human action recognition”, Science Direct Image and Vision Computing 28 (2010) 976–990. [7] Mohiuddin Ahmad, Seong-Whan Lee, “Human action recognition using shape and CLG-(Combined local-global) motion flow from multi-view image sequences”, Science Direct Pattern Recognition 41 (2008), 2237 – 2252. [8] Mohiuddin Ahmad and Seong-Whan Lee, “HMM-based Human Action Recognition Using Multiview Image Sequences”, Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), 2006 IEEE. [9] Moshe Blank, Lena Gorelick, Eli Shechtman, Michal Irani, Ronen Basri, “ Actions as space–time shapes”, Proceedings of the International Conference On Computer Vision (ICCV’05), vol. 2, Beijing, China, October 2005, pp. 1395– 1402. [10] O. Masoud and N. Papanikolopoulos, “A method for human action recognition,” IVC, Vol. 21, 2003, pp.729-743. [11] J. K. Aggarwal and Q. Cai, “Human Motion Analysis: A Review”, idealibrary: Computer Vision and Image Understanding,Vol. 73, No. 3, March 1999, pp. 428–440. [12] http://www.nada.kth.se/cvap/actions