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A methodology for extracting standing human bodies from single images

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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A methodology for extracting standing human bodies from single images

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com A METHODOLOGY FOR EXTRACTING STANDING HUMAN BODIES FROM SINGLE IMAGES By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “A Methodology for Extracting Standing Human Bodies From Single Images” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “A Methodology for Extracting Standing Human Bodies From Single Images” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “A Methodology for Extracting Standing Human Bodies From Single Images” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: Segmentation of human bodies in images is a challenging task that can facilitate numerous applications, like scene understanding and activity recognition. In order to cope with the highly dimensional pose space, scene complexity, and various human appearances, the majority of existing works require computationally complex training and template matching processes. We propose a bottom-up methodology for automatic extraction of human bodies from single images, in the case of almost upright poses in cluttered environments. The position, dimensions, and color of the face are used for the localization of the human body, construction of the models for the upper and lower body according to anthropometric constraints, and estimation of the skin color. Different levels of segmentation granularity are combined to extract the pose with highest potential. The segments that belong to the human body arise through the joint estimation of the foreground and background during the body part search phases, which alleviates the need for exact shape matching. The performance of our algorithm is measured using 40 images (43 persons) from the INRIA person dataset and 163 images from the “lab1” dataset, where the measured accuracies are 89.53% and 97.68%, respectively. Qualitative and quantitative experimental results demonstrate that our methodology outperforms state-of-the-art interactive and hybrid top-down/bottom-up approaches.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Extraction of the human body in unconstrained still images is challenging due to several factors, including shading, image noise, occlusions, background clutter, the high degree of human body deformability, and the unrestricted positions due to in and out of the image plane rotations. Knowledge about the human body region can benefit various tasks, such as determination of the human layout ,recognition of actions from static images ,and sign language recognition Human body segmentation and silhouette extraction have been a common practice when videos are available in controlled environments, where background information is available, and motion can aid the segmentation through background subtraction. In static images, However, there are no such cues, and the problem of silhouette extraction is much more challenging, especially when we are considering complex cases. Manuscript received March 3, 2014; revised July 29, 2014 and December 24, 2014; accepted January 4, 2015. Date of publication February 24, 2015; date of current version May 13, 2015. This paper was recommended by Associate Editor Y. Yuan. The authors are with the Department of Engineering, Wright State University, Dayton, OH 45435 USA .Color versions of one or more of the figures in this paper are available online Digital Object Identifier 10.1109/THMS.2015.2398582 Moreover, methodologies that are able to work at a frame level can also work for sequences of frames, and facilitate existing methods for action recognition based on silhouette features and body skeletonization. In this study, we propose a bottom-up approach for human body segmentation in static images. We decompose the problem into three sequential problems: Face detection, upper body extraction, and lower body extraction, since there is a direct pairwise correlation among them. Face detection provides a strong indication about the presence of humans in an image, greatly reduces the search space for the upper body, and provides information about skin color. Face dimensions also aid in determining the dimensions of the rest of the body, according to anthropometric constraints. This information guides the search for the upper body, which in turns leads the search for the lower body. Moreover, upper body extraction provides additional information about the position of the hands, the detection of which is very important for several applications.
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com The basic units upon which calculations are performed are super pixels from multiple levels of image segmentation. The benefit of this approach is twofold. First, different perceptual groupings reveal more meaningful relations among pixels and a higher, however, abstract semantic representation. Second, a noise at the pixel level is suppressed and the region statistics allow for more efficient and robust computations. Instead of relying on pose estimation as an initial step or making strict pose assumptions, we enforce soft anthropometric constraints to both search a generic pose space and guide the body segmentation process. An important principle is that body regions should be comprised by segments that appear strongly inside the hypothesized body regions and weakly in the corresponding background. The general flow of the methodology can be seen in Fig. 1. The major contributions of this study address upright and not occluded poses. 1) We propose a novel framework for automatic segmentation of human bodies in single images. 2) We combine information gathered from different levels of image segmentation, which allows efficient and robust computations upon groups of pixels that are perceptually correlated. 3) Soft anthropometric constraints permeate the whole process and uncover body regions. 4) Without making any assumptions about the foreground and background, except for the assumptions that sleeves are of similar color to the torso region, and the lower part of the pants is similar to the upper part of the pants, we structure our searching and extraction algorithm based on the premise that colors in body regions appear strongly inside these regions (foreground) and weakly outside (background).
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: We presented a novel methodology for extracting human bodies from single images. It is a bottom-up approach that combines information from multiple levels of segmentation in order to discover salient regions with high potential of belonging to the human body. The main component of the system is the face detection step, where we estimate the rough location of the body, construct a rough anthropometric model, and model the skin’s color. Soft anthropometric constraints guide an efficient search for the most visible body parts, namely the upper and lower body, avoiding the need for strong prior knowledge, such as the pose of the body. Experiments on a challenging dataset showed that the algorithm can outperform state-of-the-art segmentation algorithms, and cope with various types of standing everyday poses. However, we make some assumptions about the human pose, which restrict it from being applicable to unusual poses and when occlusions are strong. In the future, we intend to deal with more complex poses, without necessarily relying on strong pose prior. Problems like missing extreme regions, such as hair, shoes, and gloves can be solved by incorporation of more masks in the search for these parts, but caution should be taken in keeping the computational complexity from rising excessively
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] S. Li, H. Lu, and L. Zhang, “Arbitrary body segmentation in static images,”Pattern Recog., vol. 45, no. 9, pp. 3402–3413, 2012. [2] L. Huang, S. Tang, Y. Zhang, S. Lian, and S. Lin, “Robust human body segmentation based on part appearance and spatial constraint,”Neurocomputing, vol. 118, pp. 191–202, 2013. [3] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman,“The pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis.,vol. 88, no. 2, pp. 303–338, 2010. [4] M. P. Kumar, A. Zisserman, and P. H. Torr, “Efficient discriminativelearning of parts-based models,” in Proc. IEEE 12th Int. Conf. Comput.Vis., 2009, pp. 552–559. [5] V. Delaitre, I. Laptev, and J. Sivic, “Recognizing human actions in still images: A study of bag-of-features and part-based representations,” in Proc. IEEE Brit. Mach. Vis. Conf., 2010. [6] A. Gupta, A. Kembhavi, and L. S. Davis, “Observing human-object interactions: Using spatial and functional compatibility for recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 10, pp. 1775–1789, Oct.2009. [7] B. Yao and L. Fei-Fei, “Grouplet: A structured image representation for recognizing human and object interactions,” inProc. IEEE Conf. Comput.Vis. Pattern Recog., 2010, pp. 9–16.

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