Abstract
This paper presents a method to address the problem of long-term robust object tracking in unconstrained environments. An enhanced random fern is proposed and integrated into our tracking framework as the object detector, whose main idea is to exploit the potential distribution properties of feature vectors which are here called hidden classes by on-line clustering of feature space for each leaf-node of ferns. The kernel density estimation technique is then used to evaluate unlabeled samples based on the hidden classes which are set as the data points of the kernel function. Experimental results on challenging real-world video sequences demonstrate the effectiveness and robustness of our approach. Comparisons with several state-of-the-art approaches are provided.
Similar content being viewed by others
References
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conferences on Artificial Intelligence, vol. 81, pp. 674–679 (1981)
Isard, M., Blake, A.: CONDENSATION—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29, 5–28 (1998)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)
Lim, J., Ross, D., Lin, R., Yang, M.: Incremental learning for visual tracking. In: Neural Information Processing Systems (NIPS) (2005)
Grabner, H., Bischof, H.: On-line boosting and vision. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 260–267 (2006)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: IEEE Int’l Conf. Computer Vision (ICCV), WS on On-line Learning for Computer Vision (2009)
Wang, A., Wan, G., Cheng, Z., Li, S.: An incremental extremely random forest classifier for online learning and tracking. In: IEEE Int’l Conf. Image Processing (ICIP), pp. 1449–1452 (2009)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: European Conference on Computer Vision (ECCV) (2008)
Stalder, S., Grabner, H., van Gool, L., Zurich, E., Leuven, K.: Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition. In: IEEE Int’l Conf. Computer Vision (ICCV), WS on On-line Learning for Computer Vision (2009)
Leistner, C., Saffari, A., Santner, J., Bischof, H.: Semi-supervised random forests. In: IEEE Int’l Conf. Computer Vision (ICCV) (2009)
Leistner, C., Godec, M., Saffari, A., Bischof, H.: On-line multi-view forests for tracking. In: DAGM-Symposium, pp. 493–502 (2010)
Chapelle, O., Scholkopf, B., Zien, A. (eds.): Semi-supervised Learning. MIT Press, Cambridge (2006)
Abu-Mostafa, Y.: Machines that learn from hints. Sci. Am. 272(4), 64–71 (1995)
Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2009)
Leistner, C., Saffari, A., Bischof, H.: MILForests: multiple-instance learning with randomized trees. In: European Conference on Computer Vision (ECCV) (2010)
Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2005)
Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2008)
Ramanan, D., Forsyth, D., Zisserman, A.: Strike a pose: tracking people by finding stylized poses. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2001)
Ozuysal, M., Lepetit, V., Fleuret, F., Fua, P.: Feature harvesting for tracking-by-detection. In: European Conference on Computer Vision (ECCV) (2006)
Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: IEEE Workshop on Applications of Computer Vision (WACV) (2005)
Roth, P., Donoser, M., Bischof, H.: On-line learning of unknown hand held objects via tracking. In: Int’l Conf. Computer Vision Systems (ICVS) (2006)
Yu, Q., Dinh, T., Medioni, G.: Online tracking and reacquisition using co-trained generative and discriminative trackers. In: European Conference on Computer Vision (ECCV) (2008)
Kalal, Z., Matas, J., Mikolajczyk, K.: Online learning of robust object detectors during unstable tracking. In: IEEE Int’l Conf. Computer Vision (ICCV), WS on On-line Learning for Computer Vision (2009)
Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2010)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Ozuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2007)
Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Miami, June 2009
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 798–805 (2006)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 40672203) and the Doctoral Innovation Foundation of Southwest Jiaotong University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Quan, W., Chen, J.X. & Yu, N. Robust object tracking using enhanced random ferns. Vis Comput 30, 351–358 (2014). https://doi.org/10.1007/s00371-013-0860-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-013-0860-y