Human Activity Tracking using Star Skeleton and Activity Recognition using HMM’s and Neural...
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International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 2014 1 ISSN 2250-3153 www.ijsrp.org Human Activity Tracking using Star Skeleton and Activity Recognition using HMM’s and Neural Network Deenbandhu Singh*, Akhilesh Kumar Yadav*, Vivek Kumar * * Computer Science Department Babu Banarasi Das University, Lucknow, Uttar Pradesh, India. Abstract - Human motion detection and analysis is currently an important area of research, motion analysis help us to solve many problems. An Automated Video Surveillance Model is presented in this paper. The model which I proposed is capable of detecting and monitoring people in both environments Indoor and outdoor. The Model is capable to find out the Suspicious and Non- Suspicious activities. It also detects multiple peoples in video and monitoring their activities. Moving targets are detected and their boundaries extracted, we use star skeletonization technique with the adaptive centroid point to create human skeletons. In this paper we use HMM-based methodology for action recognition. In our proposed method, a series of star skeletons is generated according to action over time. Then, time-sequential images frame is converted into a feature vector sequence and the feature vector is translated into sequence of symbols after that we use Neural Network for action recognition which represents the particular action in Suspicious and Non-Suspicious category. We design a codebook of posture, which contains representative star skeletons of various human action types and define a star distance to find out the similarity between feature vectors. Each and every sequence of feature vector is matched against the codebook and Neural Network after that Symbol is assigned to the most similar. Then the time-sequential images or frames are converted to a sequence of symbol posture by this, we easily categorize the actions. I nde x T erms - Moving target, Star skeleton, Action recognition, HMM, Neural Network. 1. I NTRODUCTI ON Recognizing human activities from video is important applications of computer vision. This application is very useful for video surveillance, human-computer interface, industry, academia, security agencies, consumer agencies, and the general populace as well [1]. The classification of human actions has remained a challenging problem of the sheer amount of variations in the imaging conditions (view-point, illumination etc.) and attributes of the individual and therefore captures the local appearance and motion information. Methods based on local features or interest points have shown a promising result in action recognition. Most of the approaches described above advocates the use of single features of human action classification [2] [3]. In this paper, we propose a non-model- based algorithm to recognize human activity. In the proposed algorithm, we segment each frame of the input video into foreground and background regions. Morphological operations are applied to the foreground regions to fill holes, remove noise, and ensure connectivity [4]. Background subtraction is a widely used approach from detecting moving objects of videos from static cameras. Thinking of the approach is that of detecting the moving objects of the difference between the current frame and a reference frame, often called the “background model” or “background image”. As a baric, the background image must be a representation of the scene of no moving objects and must be kept regularly updated so as to adapt to the varying luminaries conditions and geometry settings [5][6]. We use the Neural Network for classify the particular action [9] [10] [11]. More complex models have extended the concept of “background subtraction” beyond its literal meaning. Background subtraction (BS) is a “quick” way of localizing moving objects. The “star” skeleton consists of only the gross extremities of the target joined to its centroid in a “star” fashion [7] [8]. HMM is a type of stochastic state transit models. By HMMs we generate the codebook and we use Neural Network for action recognizes [12] [13] then we relate them to their use for automatic pattern recognition. 2. Proposed Method This system architecture consists of three parts: 1. Feature Extraction 2. Mapping features to symbols 3. Action recognition 2.1 Feature Extraction In the Feature Extraction phase we follow the three steps which are:- Figure-1 Process flow of Feature Extraction
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International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 2014 1ISSN 2250-3153
www.ijsrp.org
Human Activity Tracking using Star Skeleton andActivity Recognition using HM M’s and Neural Network
Deenbandhu Singh*, Akhilesh Kumar Yadav*, Vivek Kumar *
* Computer Science DepartmentBabu Banarasi Das University, Lucknow, Uttar Pradesh, India.
Abstract - Human motion detection and analysis is currently animportant area of research, motion analysis help us to solve many
problems. An Automated Video Surveillance Model is presentedin this paper. The model which I proposed is capable of detectingand monitoring people in both environments Indoor and outdoor.The Model is capable to find out the Suspicious and Non-Suspicious activities. It also detects multiple peoples in video andmonitoring their activities. Moving targets are detected and their
boundaries extracted, we use star skeletonization technique withthe adaptive centroid point to create human skeletons. In this
paper we use HMM-based methodology for action recognition.In our proposed method, a series of star skeletons is generatedaccording to action over time. Then, time-sequential imagesframe is converted into a feature vector sequence and the featurevector is translated into sequence of symbols after that we use
Neural Network for action recognition which represents the particular action in Suspicious and Non-Suspicious category. Wedesign a codebook of posture, which contains representative starskeletons of various human action types and define a stardistance to find out the similarity between feature vectors. Eachand every sequence of feature vector is matched against thecodebook and Neural Network after that Symbol is assigned to
the most similar. Then the time-sequential images or frames areconverted to a sequence of symbol posture by this, we easilycategorize the actions.
I ndex T erms - Moving target, Star skeleton, Actionrecognition, HMM, Neural Network.
1. I NTRODUCTION
Recognizing human activities from video is importantapplications of computer vision. This application is very usefulfor video surveillance, human-computer interface, industry,academia, security agencies, consumer agencies, and the general
populace as well [1]. The classification of human actions has
remained a challenging problem of the sheer amount ofvariations in the imaging conditions (view-point, illuminationetc.) and attributes of the individual and therefore captures thelocal appearance and motion information. Methods based onlocal features or interest points have shown a promising result inaction recognition. Most of the approaches described aboveadvocates the use of single features of human actionclassification [2] [3]. In this paper, we propose a non-model-
based algorithm to recognize human activity. In the proposedalgorithm, we segment each frame of the input video intoforeground and background regions. Morphological operations
are applied to the foreground regions to fill holes, remove noise,and ensure connectivity [4]. Background subtraction is a widelyused approach from detecting moving objects of videos fromstatic cameras. Thinking of the approach is that of detecting themoving objects of the difference between the current frame and areference frame, often called the “background model” or“background image” . As a baric, the background image must bea representation of the scene of no moving objects and must bekept regularly updated so as to adapt to the varying luminariesconditions and geometry settings [5][6]. We use the Neural
Network for classify the particular action [9] [10] [11]. Morecomplex models have extended the concept of “backgroundsubtraction” beyond its literal meaning. Backg round subtraction(BS) is a “quick” way of localizing moving objects. The “star”skeleton consists of only the gross extremities of the target joinedto its centroid in a “star” fashion [7] [8]. HMM is a type ofstochastic state transit models. By HMMs we generate thecodebook and we use Neural Network for action recognizes [12][13] then we relate them to their use for automatic patternrecognition.
2. Proposed Method
This system architecture consists of three parts:1. Feature Extraction2. Mapping features to symbols3. Action recognition
2.1 Feature Extraction
In the Feature Extraction phase we follow the three steps which are:-
Figure-1 Process flow of Feature Extraction
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International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 2014 5ISSN 2250-3153
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R EFERENCES [1] Nizar Zarka, Ziad Alhalah and Rada Deeb, “ Real-Time Human Motion
Detection and Tracking.” .[2] Pavan Turaga , Student Member, IEEE , Rama Chellappa , Fellow, IEEE , V.
S. Subrahmanian, and Octavian Udrea , “Machine Recognition of HumanActivities: A Survey.”, VOL. 18, NO. 11.2008 .
[3] Matteo Bregonzio, Shaogang Gong and Tao Xiang, “Recognising Action asClouds of Space- Time Interest Points.”,2009 .
[4] Jianpeng Zhou and Jack Hoang, “ Real Time Robust Human Detection andTracking System”.
[5] Liang Wang, Weiming Hu, Tieniu Tan, “Recent developments in humanmotion analysis.”, Pattern Recognition 36 (2003) 585 – 601.
[6] K. Srinivasan,K. Porkumaran and G. Sainarayanan, ” Intelligent human body tracking, modeling, and activity analysis of video surveillance system:A survey” Vol.1 December 2009 pp 1-8.
[7] Hsuan-Sheng Chen, Hua-Tsung Chen, Yi-Wen Chen and Suh-Yin Lee,“Human Action Recognition Using Star Skeleton”. VSSN'06, October 27,2006, Santa Barbara, California, USA. Copyright 2006 ACM 1-59593-496-0/06/0010...$5.00.
[8] Hironobu Fujiyoshi and Alan J. Lipton, “ Real-time Human Motion Analysis by Image Skeletonization”.
[9] Ani1 K. Jain ,Jianchang Mao and K.M. Mohiuddin , “Artificial Neural Network: A Tutorial.”. 0018-9162/96/$5.000 1996 IEEE
[10] Heiko Hoffmann, Michael D. Howard, and Michael J. Daily, “Fast patternmatching with time- delay neural networks”.
[11] Hazem M. El-Bakry and Nikos Mastorakis, “Fast Human Motion Tracking by using High Speed Neural Networks”. ISSN: 1790-5109 ,ISBN: 978-960-474-114-4.
[12] Barbara Resch, “Hidden Markov Models”. [13] Eric Fosler_Lussier, “Markov Models and Hidden Markov Models_ A Brief
Tutorial ”.
AUTHORS First Author – Deenbandhu Singh, M.tech, Computer ScienceDepartment Babu Banarasi Das University, Lucknow, UttarPradesh, India [email protected]
Second Author – Akhilesh Kumar Yadav, M.tech ComputerScience Department Babu Banarasi Das University, Lucknow,Uttar Pradesh, India [email protected]
Third Author – Vivek Kumar, M.tech (Sr. Lecturer), ComputerScience Department Babu Banarasi Das University, Lucknow,Uttar Pradesh, India [email protected]
Correspondence Author Deenbandhu Singh,[email protected] [email protected] Contact no.9454668669, 8604663451
