Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching

Abdul Basit, Matthew N. Dailey, Pudit Laksanacharoen, Jednipat Moonrinta

Abstract

Most visual tracking algorithms lose track of the target object (start tracking a different object or part of the background) or report an error when the object being tracked leaves the scene or becomes occluded in a cluttered environment. We propose a fast algorithm for mobile robots tracking humans or other objects in real-life scenarios to avoid these problems. The proposed method uses an adaptive histogram threshold matching algorithm to suspend the CAMSHIFT tracker when the target is insufficiently clear. While tracking is suspended, any method would need to continually scan the entire image in an attempt to redetect and reinitialize tracking of the specified object. However, searching the entire image for an arbitrary target object requires an extremely efficient algorithm to be feasible in real time. Our method, rather than a detailed search over the entire image, makes efficient use of the backprojection of the target object’s appearance model to hypothesize and test just a few candidate locations for the target in each image. Once the target object is redetected and sufficiently clear in a new image, the method reinitializes tracking. In a series of experiments with four real-world videos, we find that the method is successful at suspending and reinitializing CAMSHIFT tracking when the target leaves and reenters the scene, with successful reinitialization and very low false positive rates.

References

  1. Allen, J. G., Xu, R. Y. D., and Jin, J. S. (2004). Object tracking using camshift algorithm and multiple quantized feature spaces. In Pan-Sydney Area Workshop on Visual Information Processing, volume 36, pages 3-7.
  2. Basit, A., Dailey, M. N., and Laksanacharoen, P. (2012). Model driven state estimation for target pursuit. In International Conference on Control, Automation, Robotics & Vision (ICARCV), 2012 IEEE Conference on, pages 1077-1082.
  3. Bradski, G. (Oct). Real time face and object tracking as a component of a perceptual user interface. In Applications of Computer Vision, 1998. WACV 7898. Proceedings., Fourth IEEE Workshop on, pages 214-219.
  4. Chen, X., Huang, H., Zheng, H., and Li, C. (2008). Adaptive bandwidth mean shift object detection. In Robotics, Automation and Mechatronics, 2008 IEEE Conference on, pages 210-215.
  5. Chen, X., Huang, Q., Hu, P., Li, M., Tian, Y., and Li, C. (2009). Rapid and precise object detection based on color histograms and adaptive bandwidth mean shift. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 4281- 4286.
  6. Comaniciu, D., Ramesh, V., and Meer, P. (2000). Realtime tracking of non-rigid objects using mean shift. In IEEE conference on Computer Vision and Pattern Recognition, 2000. Proceedings., volume 2, pages 142-149.
  7. Comaniciu, D., Ramesh, V., and Meer, P. (2003). Kernelbased object tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):564-577.
  8. Denman, S., Chandran, V., and Sridharan, S. (2007). An adaptive optical flow technique for person tracking systems. Pattern Recognition Letters, 28(10):1232- 1239.
  9. Emami, E. and Fathy, M. (2011). Object tracking using improved camshift algorithm combined with motion segmentation. In Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pages 1-4.
  10. Exner, Bruns, Kurz, Grundhfer, and Bimber (2010). Fast and robust camshift tracking. In Proceedings of IEEE International Workshop on Computer Vision for Computer Games (IEEE CVCG).
  11. Nouar, O.-D., Ali, G., and Raphael, C. (2006). Improved object tracking with camshift algorithm. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, volume 2, pages II-II.
  12. Perreault, S. and Hebert, P. (2007). Median filtering in constant time. Image Processing, IEEE Transactions on, 16(9):2389-2394.
  13. Porikli, F. (2005). Integral histogram: a fast way to extract histograms in cartesian spaces. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 829-836.
  14. Sizintsev, M., Derpanis, K., and Hogue, A. (2008). Histogram-based search: A comparative study. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8.
  15. Ta, D.-N., Chen, W.-C., Gelfand, N., and Pulli, K. (2009). Surftrac: Efficient tracking and continuous object recognition using local feature descriptors. In IEEE conference on Computer Vision and Pattern Recognition., pages 2937-2944.
  16. Yokoyama, M. and Poggio, T. (2005). A contour-based moving object detection and tracking. In Visual Surveillance and Performance Evaluation of Tracking and Surveillance., pages 271-276.
  17. Zhou, H., Yuan, Y., and Shi, C. (2009). Object tracking using sift features and mean shift. Computer Vision and Image Understanding, 113(3):345-352.
Download


Paper Citation


in Harvard Style

Basit A., N. Dailey M., Laksanacharoen P. and Moonrinta J. (2014). Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 507-514. DOI: 10.5220/0004670605070514


in Bibtex Style

@conference{visapp14,
author={Abdul Basit and Matthew N. Dailey and Pudit Laksanacharoen and Jednipat Moonrinta},
title={Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={507-514},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004670605070514},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching
SN - 978-989-758-009-3
AU - Basit A.
AU - N. Dailey M.
AU - Laksanacharoen P.
AU - Moonrinta J.
PY - 2014
SP - 507
EP - 514
DO - 10.5220/0004670605070514