Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30067
Detection and Pose Estimation of People in Images

Authors: Mousa Mojarrad, Amir Masoud Rahmani, Mehrab Mohebi

Abstract:

Detection, feature extraction and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes and the high dimensionality of articulated body models and also the important field in Image, Signal and Vision Computing in recent years. In this paper, four types of people in 2D dimension image will be tested and proposed. The system will extract the size and the advantage of them (such as: tall fat, short fat, tall thin and short thin) from image. Fat and thin, according to their result from the human body that has been extract from image, will be obtained. Also the system extract every size of human body such as length, width and shown them in output.

Keywords: Analysis of Image Processing, Canny Edge Detection, Human Body Recognition, Measurement, Pose Estimation, 2D Human Dimension.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330207

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF

References:


[1] D.M. Gavrila, "The Visual Analysis of Human Movement: A Survey,"Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82-98, 1999.
[2] Brett Allen1. And Brian Curless. And Zoran Popovi. And Aaron Hertzmann.2006." Learning a correlated model of identity and posedependent body shape variation for real-time synthesis," Eurographics/ ACM SIGGRAPH Symposium on Computer Animation 2006.
[3] KADYROV, M. PETROU: "The Trace Transform and Its Applications", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.8, pp. 811-828, August 2001
[4] N. FEDOTOV, L. SHULGA: "New Theory of Pattern Recognition on the Basis of Stochastic Geometry", in Proc. WSCG 2000, the 8-th International Conference in Central Europe on Computer Graphics, Visualisation and Digital Media 2000, Plzen, Czech Republic, Feb. 2000.
[5] G. WOLBERG, S. ZOKAI: "Robust Image Registration using Log-polar Transform", wwwcs. engr.ccny.edu/~wolberg/pub/icip00.pdf (last visit 20.7.2003).
[6] S. Ju, M. Black and Y. Yacoob. Cardboard people: A parameterized model of articulated motion. Int. Conf. on Automatic Face and Gesture Recognition, pp. 38-44, 1996.
[7] Y. Wu, G. Hua and T. Yu, Tracking articulated body by dynamic Markov network, ICCV, pp. 1094-1101, 2003.
[8] C. Sminchisescu and B. Triggs. Covariance scaled sampling for monocular 3D body tracking, CVPR, vol. 1 pp. 447-454, 2001.
[9] H. Sidenbladh, M. Black and D. Fleet. Stochastic tracking of 3D human figures using 2D image motion, ECCV, vol. 2, pp. 702-718, 2000.
[10] J. MacCormick and M. Isard. Partitioned sampling, articulated objects, and interface-quality hand tracking. ECCV (2), pp. 3-19, 2000.
[11] M. Burl, M. Weber and P. Perona . A probabilistic approach to object recognition using local photometry and global geometry, ECCV, pp. 628-641, 1998.
[12] S. Ioffe and D. Forsyth. Probabilistic methods for finding people, IJCV 43(1):45-68, 2001.
[13] D. Ramanan and D. Forsyth. Finding and tracking people from the bottom up, CVPR, Vol. II, pp. 467-716, 2003.
[14] S. Yu, R. Gross, and J. Shi. Object segmentation by graph partitioning Concurrent object recognition and segmentation by graph partitioning, Advances in Neural Info. Proc. Sys. 15, pp. 1407-1414, 2003.
[15] P. Felzenszwalb and D. Huttenlocher. Efficient matching of pictorial structures, CVPR, Vol. 2, pp. 66-73, 2000.
[16] M. Jordan, T. Sejnowski and T. Poggio. Graphical models: Foundations of neural computation, MIT Press, 2001.
[17] Hochberg, Julian E. andVirginiaBrooks, "Pictorial recognition as an unlearned ability: A study of one child-s performance," American Journal of Psychology, 75 (1962), 624-628.
[18] Barnard, Stephen T., "Interpreting perspective images," Artificial Intelligence, 21 (1983), 435-462.
[19] Lowe, David G., and Thomas O. Binford, "The recovery of threedimensional structure from image curves," IEEE Trans. on Pattern Analysis and Machine Intelligence, 7, 3 (May 1985), 320-326.
[20] M. Oren, C. Papageorgiour, P. Sinha, E. Osuma, and T. Poggio." Pedestrian detection using wavelet templates". In Proc. Comp. Vis. and Pattern Rec., pages 193-199. IEEE, 1997.
[21] D. Gavrila. Pedestrian detection from a moving vehicle. In Proc. European Conf. Comp. Vis., pages 37-49, 2000.
[22] P. Viola, M. Jones, and D. Snow. "Detecting pedestrians using patterns of motion and appearance". In Proc. Int. Conf. Comp. Vis., pages 734- 741, 2003.
[23] Lipton, H. Fujiyoshi, and R. Patil. "Moving target classification and tracking from real-time video". In Proc. Wkshp. Applications of Comp. Vis., 1998.
[24] Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik. "Recognizing objects in range data using regional point descriptors". In Proc. of the Europ. Conf. on Computer Vision (ECCV), 2004.
[25] Ajmal S. Mian, Mohammed Bennamoun, and Robyn A. Owens. "Matching tensors for automatic correspondence and registration". In Proc. of the Europ. Conf. on Computer Vision (ECCV), 2004.
[26] Z. Wu, Y. Wang, and G. Pan. "3D face recognition using local shape map". In Proc. of IEEE Intern. Conf. on Image Processing, pages 2003- 2006, 2004.
[27] ] X. Li and I. Guskov. "Multiscale features for approximate alignment of point-based surfaces". In Symp. on Geometry Processing, pages 217- 226, 2005.
[28] R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. "Matching 3D models with shape distributions. In Shape Modeling Internationa"l, pages 154- 166, 2001.
[29] D.G. Lowe: "Distinctive image features from scale-invariant keypoints" accepted paper, Int. J. of Comp. Vision, 2004.
[30] M. Weber: "Unsupervised Learning of Models for Object Recognition", Ph.D thesis, Department of Computation and Neural Systems, California Institute of Technology, Pasadena, CA, 2000
[31] L. Fei-Fei, R. Fergus and P. Perona: "A bayesian approach to unsupervised one-shot learning of object categories", Proc. Int. Conf. on Comp. Vision, Nice, France, 2003.
[32] S. Ioffe and d.a. Forsyth.2001."Probabilistic Methods for Finding People," International Journal of Computer Vision 43(1), 45-68, 2001.
[33] Gonzales R.C.and Woods R.E, (1992)."Digital Image Processing", USA, Addison-wesley.