Abstract
Object tracking is an important topic in the field of computer vision. Commonly used color-based trackers are based on a fixed set of color features such as RGB or HSV and, as a result, fail to adapt to changing illumination conditions and background clutter. These drawbacks can be overcome to an extent by using an adaptive framework which selects for each frame of a sequence the features that best discriminate the object from the background. In this paper, we use such an adaptive feature selection method embedded into a particle filter mechanism and show that our tracking method is robust to lighting changes and background distractions. Different experiments also show that the proposed method outperform other approaches.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects using Mean Shift. In: Computer Vision and Pattern Recognition (CVPR), pp. 142–149. IEEE Computer Society (2000)
Ho, J., Lee, K.C., Yang, M.H., Kriegman, D.J.: Visual Tracking Using Learned Linear Subspaces. In: Computer Vision and Pattern Recognition (CVPR), pp. 782–789. IEEE Computer Society (2004)
Zarit, B.D., Super, B.J., Quek, F.K.H.: Comparison of Five Colour Models in Skin Pixel Classification. In: International Conference in Computer Vision (ICCV), pp. 58–63 (1999)
Grabner, H., Grabner, M., Bischof, H.: Realtime Tracking via On-line Boosting. In: British Machine Vision Conference (BMVC), pp. 47–56 (2006)
Sidibe, D., Fofi, D., Meriaudeau, F.: Using Visual Saliency For Object Tracking with Particle Filter. In: European Signal Processing Conference (EUSIPCO), pp. 1776–1780 (2010)
Leung, A.P., Gong, S.: Online Feature Selection using Mutual Information for Real-time Multi-view Object Tracking. In: Zhao, W., Gong, S., Tang, X. (eds.) AMFG 2005. LNCS, vol. 3723, pp. 184–197. Springer, Heidelberg (2005)
Collins, R.T., Liu, Y.: On-line Selection of Discriminative Tracking Features. In: International Conference in Computer Vision (ICCV), pp. 346–352 (2003)
Han. B., Davis. L: Object Tracking by Adaptive Feature Extraction. In: International Conference in Computer Vision (ICCV), pp. 1501–1504 (2004)
Kwolek, B.: Object Tracking using Discriminative Feature Selection. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 287–298. Springer, Heidelberg (2006)
Nummiaro, K., Koller-Meier, E., Van Gool, L.J.: An Adaptive Color-based Particle Filter. In: Image and Vision Computing, pp. 99–110 (2003)
Isard, M., Blake, A.: Condensation - Conditional Density Propagation for Visual Tracking. Int. Journal of Computer Vision (IJCV) 28, 99–110 (1998)
Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 32, 1582–1596 (2010)
Stern, H., Efros, B.: Adaptive Color Space Switching for Tracking under Varying Illumination. Image and Vision Computing 23, 3–346 (2005)
Ross, D., Lim, J., Lin, R.S., Yang, M.H.: Incremental Learning for Robust Visual Tracking. Int. Journal of Computer Vision (IJCV) 77, 125–141 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Venkatrayappa, D., Sidibé, D., Meriaudeau, F., Montesinos, P. (2014). Adaptive Feature Selection for Object Tracking with Particle Filter. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-11755-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11754-6
Online ISBN: 978-3-319-11755-3
eBook Packages: Computer ScienceComputer Science (R0)