Who is the Hero? - Semi-supervised Person Re-identification in Videos

Umar Iqbal, Igor D. D. Curcio, Moncef Gabbouj

Abstract

Given a crowd-sourced set of videos of a crowded public event, this paper addresses the problem of detecting and re-identifying all appearances of every individual in the scene. The persons are ranked according to the frequency of their appearance and the rank of a person is considered as the measure of his/her importance. Grouping appearances of every person from such videos is a very challenging task. This is due to unavailability of prior information or training data, large changes in illumination, huge variations in camera viewpoints, severe occlusions and videos from different photographers. These problems are made tractable by exploiting a variety of visual and contextual cues i.e., appearance, sensor data and co-occurrence of people. A unified framework is proposed for efficient person matching across videos followed by their ranking. Experimental results on two challenging video data sets demonstrate the effectiveness of the proposed algorithm.

References

  1. Ahonen, T., Hadid, A., and Pietikainen, M. (2004). Face Recognition with Local Binary Patterns. In European Conference on Computer Vision.
  2. Barr, J. R., Bowyer, K. W., and Flynn, P. J. (2011). Detecting questionable observers using face track clustering. In IEEE Workshop on Applications of Computer Vision.
  3. Bauml, M., Bernardin, K., Fischer, M., Ekenel, H., and Stiefelhagen, R. (2010). Multi-pose face recognition for person retrieval in camera networks. In IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 441-447.
  4. Bäuml, M., Tapaswi, M., and Stiefelhagen, R. (2013). Semi-supervised Learning with Constraints for Person Identification in Multimedia Data. In IEEE Conference on Computer Vision and Pattern Recognition.
  5. Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools.
  6. Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2.
  7. Cinbis, R. G., Verbeek, J., and Schmid, C. (2011). Unsupervised Metric Learning for Face Identification in TV Video. In International Conference on Computer Vision, Barcelona, Spain.
  8. Comaniciu, D., Ramesh, V., and Meer, P. (2003). Kernelbased object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):564-577.
  9. Cricri, F., Curcio, I. D. D., Mate, S., Dabov, K., and Gabbouj, M. (2012). Sensor-based analysis of user generated video for multi-camera video remixing. In IEEE 18th International Conference on Multimedia Modeling, pages 255-265.
  10. Gou, G., Huang, D., and Wang, Y. (2012). A novel video face clustering algorithm based on divide and conquer strategy. In Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence, pages 53-63.
  11. Hao, P. and Kamata, S. (2012). Unsupervised people organization and its application on individual retrieval from videos. In 21st International Conference on Pattern Recognition, pages 2001-2004.
  12. Klein, D., Kamvar, S. D., and Manning, C. D. (2002). From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In International Conference on Machine Learning, pages 307-314, San Francisco, CA, USA.
  13. Kumar, N., Belhumeur, P. N., and Nayar, S. K. (2008). FaceTracer: A Search Engine for Large Collections of Images with Faces. In European Conference on Computer Vision, pages 340-353.
  14. Kumar, N., Berg, A., Belhumeur, P., and Nayar, S. (2011). Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):1962-1977.
  15. Lo Presti, L. and La Cascia, M. (2012). An on-line learning method for face association in personal photo collection. Image and Vision Computing, 30(4-5):306-316.
  16. Sivic, J., Zitnick, C. L., and Szeliski, R. (2006). Finding people in repeated shots of the same scene. In British Machine Vision Conference.
  17. Suh, B. and Bederson, B. B. (2004). Semi-automatic image annotation using event and torso identification. Technical report, Computer Science Department, University of Maryland, College Park, MD.
  18. Tao, J. and Tan, Y.-P. (2008). Efficient clustering of face sequences with application to character-based movie browsing. In IEEE International Conference on Image Processing, pages 1708-1711.
  19. Ur?ic?ár?, M., Franc, V., and Hlavác?, V. (2012). Detector of facial landmarks learned by the structured output SVM. In Proceedings of the 7th International Conference on Computer Vision Theory and Applications.
  20. Zhang, L., Chen, L., Li, M., and Zhang, H. (2003). Automated annotation of human faces in family albums. In Proceedings of the eleventh ACM international conference on Multimedia, pages 355-358.
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Paper Citation


in Harvard Style

Iqbal U., D. D. Curcio I. and Gabbouj M. (2014). Who is the Hero? - Semi-supervised Person Re-identification in Videos . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 162-173. DOI: 10.5220/0004738801620173


in Bibtex Style

@conference{visapp14,
author={Umar Iqbal and Igor D. D. Curcio and Moncef Gabbouj},
title={Who is the Hero? - Semi-supervised Person Re-identification in Videos},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={162-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004738801620173},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Who is the Hero? - Semi-supervised Person Re-identification in Videos
SN - 978-989-758-004-8
AU - Iqbal U.
AU - D. D. Curcio I.
AU - Gabbouj M.
PY - 2014
SP - 162
EP - 173
DO - 10.5220/0004738801620173