Abstract
This paper proposes a face tracking method which uses the concepts of multiple instances and Online Adaboost to successively train and update a face tracking model (FTM) for each tracked face, and then tracks the tracked faces among the image frames. In order to avoid the continuous accumulation of tracking error, this method performs a face detection process around each tracked face. This paper also provides a novel tracking recovery approach when the tracked faces are occluded with other faces. First, it uses an occlusion detection criterion to realize whether a tracked face is occluded by other objects (called shelter) or not. If it is, several candidate regions around each shelter will be constructed, and by using frame difference and skin detection information, the candidate regions will be further filtered out so that the regions with no face clues will be omitted. For the remaining regions, it will apply the FTM preserved just before occlusion to detect whether a tracked face has reappeared or not. If an occluded face indeed has reappeared, it will continue to track this face from its reappeared position. Experiments show that the proposed method provides very good face tracking results on different videos and outperforms both Camshift and Kalman Filters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Wu, M., Peng, X.: Spatio-temporal context for codebook-based dynamic background subtraction. Int. J. Electron. Commun. (2009)
Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Processing 9, 287–290 (2000)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
Nummiaro, K., Koller-Meier, E., Gool, L.V.: An Adaptive Color-Based Particle Filter. Image and Vision Computing 21, 99–110 (2003)
Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)
Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)
Zeisl, B., Leistner, C., Saari, A., Bischof, H.: Online semi-supervised multiple-instance boosting. IEEE Conference on Computer Vision and Pattern Recognition (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, YS., Chang, CI. (2016). Multi-face Tracking with Occlusion Recovery. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-23204-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-23204-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23203-4
Online ISBN: 978-3-319-23204-1
eBook Packages: EngineeringEngineering (R0)