Robust Mean Shift Tracking with Background Information
The background-weighted histogram (BWH) has been proposed in mean shift tracking algorithm to reduce the interference of background in target localization. However, the BWH also reduces the weight for part of complex object. Mean shift with BWH model is unable to track object with scale change. In this paper, we integrate an object/background likelihood model into the mean shift tracking algorithm. Experiments on both synthetic and real world video sequences demonstrate that the proposed method could effectively estimate the scale and orientation changes of the target. The proposed method can still robustly track the object when the target is not well initialized.
KeywordsObject tracking Mean shift Gaussian mixture model Background information
Unable to display preview. Download preview PDF.
- 3.Bradski, G.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal 2, 1–15 (1998)Google Scholar
- 5.Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean shift tracking with corrected background-weighted histogram. IET Computer Vision (2010)Google Scholar
- 6.Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. IET Computer Vision (2011)Google Scholar
- 7.Zivkovic, Z., Krose, B.: An EM-like algorithm for color-histogram-based object tracking. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 798–803 (2004)Google Scholar
- 8.Collins, R.: Mean-Shift Blob Tracking through Scale Space. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 234–240 (2003)Google Scholar
- 9.McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley and Sons (2000)Google Scholar
- 10.SOAMST code, http://www.comp.polyu.edu.hk/~cslzhang/SOAMST.html
- 11.EM-Shift code, http://staff.science.uva.nl/~zivkovic/PUBLICATIONS.html