Journal of Applied Information Science

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Studies on Various Type of Human Detection Algorithms for Multiple and Occluded Persons in Static Images


Affiliations
1 Dept. of CS, College of Computer Science & Information Systems, Jazan University, Saudi Arabia
2 Dept. of ECE, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India
     

   Subscribe/Renew Journal


Detecting and tracking human in still or video images provides a promising technology development and solution to many real world problems. Moreover, detecting human may be the first step to put forward the next logical steps for many applications. But, it is a challenging task due pose, dresses, color and occlusion. This paper proposes a study of human detection in static images in different view. In the literature, numerous works had been proposed to detect a single human in an image. So, the survey has been conducted for detection of multiple humans without occlusion, detection of multiple human with occlusion and human detection in fused image. Due to the difficulties found during the process of human detection such as occlusion and shadow, people in group, main focus has been given to multiple-human detection.

Keywords

Human Detection, Pose, Occlusion, Fusion, Machine Learning, Object Detection, Feature Extraction.
Subscription Login to verify subscription
User
Notifications
Font Size


  • N. Dalal, and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886-893, June 2005.

  • Q. Tian,, Zhou B, W.-H. Zhao, Y. Wei, and W.-W. Fei, “Human detection using HOG features of head and shoulder based on depth map,” JSW, vol. 8, no. 9, pp. 2223-2230, 2013.

  • B. Wu, and R. Nevatia, “Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors,” IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 90-97, 2005.

  • S. Rujikietgumjorn, and R. T. Collins, “Optimized pedestrian detection for multiple and occluded people,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3690-3697, 2013

  • S. Kakade, N. J. Uke, and N. Jain, “Real time human detection using hog feature as human descriptor and variable size sliding window,” Advances in Computer Science and Information Technology (ACSIT), vol. 1, no. 1, pp. 27-29, Oct. 2014.

  • Y. Xu, L. Xu, D. Li, and Y. Wu, “Pedestrian detection using background subtraction assisted support vector machine,” Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference, Cordoba, pp. 837-842, Nov. 2011.

  • Q. Zhu, M. C. Yeh, K. T. Cheng, and S. Avidan, “Fast human detection uses a cascade of histograms of oriented gradients,” In CVPR ’06: Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1491-1498. Washington, DC, USA: IEEE Computer Society, 2006.

  • P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” In Proceedings of IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 734-741, 2003.

  • C. Papageorgiou, and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis., vol. 38, no. 1, pp. 15-33, Jun. 2000.

  • Y.-T. Chen, and C.-S. Chen, “Fast human detection using a novel boosted cascading structure with meta stages,” IEEE Transactions on Image Processing, vol. 17, no. 8, pp. 1452-1464.

  • S. L. Phung, and A. Bouzerdoum, “Detecting people in images: An edge density approach,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2007.

  • K. M. Hofmann, and G. Rigoll, “Late fusion for person detection in camera networks,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 41-46, 2011.

  • G. Shu, A. Dehghan, O. Oreifej, E. Hand and M. Shah, “Part-based Multiple-person tracking with partial occlusion handling,” In Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

  • I. Huerta, G. Ferrer, F. Herrero, A. Prati, & A. Sanfeliu, “Multimodal feedback fusion of laser, image and temporal information,” Proceedings of the International Conference on Distributed Smart Cameras (ICDSC), 2014.

  • O. Tuzel, FPorikli F and Meer p, “Human detection via classification on riemannian manifolds”, in Conference on Computer Vision and Pattern Recognition (CVPR), 2007.

  • S. Munder, and D. Gavrila, “An experimental study on pedestrian classification,” PAMI, vol. 28, no. 11, pp. 1863-1868, 2006.

  • C. C. Chang, and C. J. Lin, “LIBSVM: A library for support vector machines”, 2001. Software Available at www.csie.ntu.edu.tw/ cjlin/libsvm.

  • Z. Lin, and L. S. Davis, “A pose-invariant descriptor for human detection and segmentation,” In ECCV, 2008.

  • J. S. Lim, “Two-dimensional signal and image processing,” Communications and Radar, p. 710, Englewood Cliffs, NJ, Prentice Hall, 1990.

  • S. Nigam, and A. Khare “Multiresolution approach for multiple human detection using moments and local binary patterns,” International Conference on Computational Vision and Robotics (ICCVR), 2014.

  • JongSeok Lim and WookHyun Kim, “Detection of Multiple Humans Using Motion Information and Adaboost Algorithm based on Harr-like Features”, International Journal of Hybrid Information Technology (IJHIT), vol. 5, no. 2, April 2012.

  • H. Bischof, and C. Beleznai, “Fast human detection in crowded scenes by contour integration and local shape estimation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2246-2253, June 2009.

  • A. Heili, C. Chen, and J.-M. Odobez, “Detection-based multi-human tracking using a CRF model,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1673-1680, Nov. 2011.

  • X. Wang, T. X. Han, and S. Yan, “An HOG-LBP Human Detector with Partial Occlusion Handling,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 32-39, Oct. 2009.

  • W. R. Schwartz, A. Kembhavi, D. Harwood and L. S. Davis, “Human detection using partial least squares analysis,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24-31, Oct. 2009.

  • P. Doll´ar, C. Wojek, B. Schiele and P. Peron, “Pedestrian detection: A Benchmark,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 304-311, June 2009.

  • J. Yaoa, and J.-M. Odobez, “Fast Human Detection from Joint Appearance and Foreground Feature Subset Covariances,” Computer Vision and Image Understanding (CVIU), vol. 115, no. 10, pp. 1414-1426, Oct. 2011.

  • S. Ikemura, and H. Fujiyoshi, “Real-Time Human Detection using Relational Depth Similarity Features,” Asian Conference on Computer Vision (ACCV), 2010.

  • S. Tang, M. Andriluka, and B. Schiele, “Detection and Tracking of Occluded People,” International Journal of Computer Vision (IJCV), 2014.

  • E. Goubet, J. Katz, and F. Porikli, “Pedestrian tracking using thermal infrared imaging,” Defense and Security Symposium. International Society for Optics and Photonics, 2006.

  • Grassi, Ana Perez, Vadim Frolov and Fernando Puente Leon, “Information fusion to detect and classify pedestrians using invariant features,” Information Fusion, vol. 12, no. 4, pp. 284-292, 2011.

  • B. Zhu, Y. Ding, J. Hua, K. Hao, and L. Hong, “Fusion of depth and vision information for human detection,” Journal of Computational Information Systems, vol. 9, no. 20, pp. 8147-8154. 2013.

  • G. E. Thomas, P. D. Frazier, and M. F. Chouikha, “Improved human detection using image fusion,” Proceedings of the IEEE ICRA Workshop on People Detection and Tracking, Kobe, Japan. 2009.

  • I. Huerta, G. Ferrer, F. Herrero, A. Prati, & A. Sanfeliu, “Multimodal feedback fusion of laser, image and temporal information,” In Proceedings of the International Conference on Distributed Smart Cameras, p. 25, ACM, 2014.

  • T. T. Zin, H. Takahashi, H. Hama, and T. Toriu, “Fusion of infrared and visible images for robust person detection,” INTECH Open Access Publisher, pp. 1-26, 2011.

  • H. Ju, and B. Bhanu, “Fusion of color and infrared video for moving human detection,” Pattern Recognition, vol. 40, no. 6, pp. 1771-1784, 2007.

  • B. Bhanu, and X. Zou, “Moving human detection based on multi-modal sensor fusion,” In Computer Vision and Pattern Recognition Workshop, (CVPRW), pp. 136-136., 2004.


Abstract Views: 339

PDF Views: 8




  • Studies on Various Type of Human Detection Algorithms for Multiple and Occluded Persons in Static Images

Abstract Views: 339  |  PDF Views: 8

Authors

M. Shanmugasundaram
Dept. of CS, College of Computer Science & Information Systems, Jazan University, Saudi Arabia
N. Shanmuga Vadivu
Dept. of ECE, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Abstract


Detecting and tracking human in still or video images provides a promising technology development and solution to many real world problems. Moreover, detecting human may be the first step to put forward the next logical steps for many applications. But, it is a challenging task due pose, dresses, color and occlusion. This paper proposes a study of human detection in static images in different view. In the literature, numerous works had been proposed to detect a single human in an image. So, the survey has been conducted for detection of multiple humans without occlusion, detection of multiple human with occlusion and human detection in fused image. Due to the difficulties found during the process of human detection such as occlusion and shadow, people in group, main focus has been given to multiple-human detection.

Keywords


Human Detection, Pose, Occlusion, Fusion, Machine Learning, Object Detection, Feature Extraction.

References