Human Skeleton Detection from Semi-constrained Environment Video

Palwasha Afsar, Paulo Cortez, Henrique Santos

2017

Abstract

The correct classification of human skeleton from video is a key issue for the recognition of human actions and behavior. In this paper, we present a computational system for a passive detection of human star skeleton from raw video. The overall system is based on two main modules: segmentation and star skeleton detection. For each module, several computer vision methods were adjusted and tested under a comparative analysis that used a challenging video dataset (e.g., different daylight and weather conditions). The obtained results show that our system is capable of detecting human skeletons in most situations.

References

  1. Afsar, P., Cortez, P., and Santos, H. (2015a). Automatic human action recognition from video using hidden markov model. In Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, pages 105-109. IEEE.
  2. Afsar, P., Cortez, P., and Santos, H. (2015b). Automatic visual detection of human behavior: A review from 2000 to 2014. Expert Systems with Applications, 42(20):6935-6956.
  3. Chen, H.-S., Chen, H.-T., Chen, Y.-W., and Lee, S.-Y. (2006). Human action recognition using star skeleton. In Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pages 171-178. ACM.
  4. and Duque, D., Santos, H., and Cortez, P. (2005). Moving object detection unaffected by cast shadows, highlights and ghosts. In IEEE International Conference on Image Processing 2005, volume 3, pages III-413. IEEE.
  5. Enzweiler, M. and Gavrila, D. M. (2009). Monocular pedestrian detection: Survey and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2179-2195.
  6. Fujiyoshi, H., Lipton, A. J., and Kanade, T. (2004). Realtime human motion analysis by image skeletonization. IEICE TRANSACTIONS on Information and Systems, 87(1):113-120.
  7. Geronimo, D., Lopez, A. M., Sappa, A. D., and Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1239- 1258.
  8. KaewTraKulPong, P. and Bowden, R. (2002). An improved adaptive background mixture model for realtime tracking with shadow detection. In Video-Based Surveillance Systems, pages 135-144. Springer.
  9. Orrite-Urunuela, C., del Rincon, J. M., Herrero-Jaraba, J. E., and Rogez, G. (2004). 2d silhouette and 3d skeletal models for human detection and tracking. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 4, pages 244-247. IEEE.
  10. Otsu, N. (1975). A threshold selection method from graylevel histograms. Automatica, 11(285-296):23-27.
  11. Poppe, R. (2007). Vision-based human motion analysis: An overview. Computer vision and image understanding, 108(1):4-18.
  12. Turaga, P., Chellappa, R., Subrahmanian, V. S., and Udrea, O. (2008). Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 18(11):1473-1488.
  13. Vemulapalli, R., Arrate, F., and Chellappa, R. (2016). R3dg features: Relative 3d geometry-based skeletal representations for human action recognition. Computer Vision and Image Understanding.
  14. Yang, X. and Tian, Y. (2014). Effective 3D action recognition using eigenjoints. Journal of Visual Communication and Image Representation, 25(1):2-11.
Download


Paper Citation


in Harvard Style

Afsar P., Cortez P. and Santos H. (2017). Human Skeleton Detection from Semi-constrained Environment Video . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 384-389. DOI: 10.5220/0006245803840389


in Bibtex Style

@conference{visapp17,
author={Palwasha Afsar and Paulo Cortez and Henrique Santos},
title={Human Skeleton Detection from Semi-constrained Environment Video},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={384-389},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006245803840389},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Human Skeleton Detection from Semi-constrained Environment Video
SN - 978-989-758-226-4
AU - Afsar P.
AU - Cortez P.
AU - Santos H.
PY - 2017
SP - 384
EP - 389
DO - 10.5220/0006245803840389