Abstract
Accounting for most recent tracking algorithms just only handle one specified challenge, in order to adjust to diverse scenarios in object tracking, we propose a discriminative tracking algorithm based on a collaborative model. In order to account for drastic appearance change, the visual prior have been learned offline by adding the locality regularization term. We transfer the visual prior to represent object and learn a basic discriminative classifier. Next we employ minimal sparse reconstruction error to find the best candidate with the learned classifier. In addition, we derive a parameter update strategy which is based on the candidates’ distribution. With this strategy, the candidates’ weight can be calculated according to the candidates’ distribution online. The tracking is carried out within a Bayesian inference framework with this representation. We use the learned classifier and sparse template to construct the dynamic parameter observation model. Furthermore, the particle filter is used to estimate the tracking result sequentially. Both qualitative and quantitative evaluations on variety of challenging benchmark sequences demonstrate that the proposed tracking algorithm achieves more robust object tracking than the state-of-the-art methods.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Adam A, Rivlin E, Shimshoni I (2003) Robust fragments-based tracking usingthe integral histogram. In CVPR 798–805
Avidan S (2001) Support vector tracking. In Proc IEEE Comput Vis Pattern Recognit Conf. Jerusalem, Israel 1: 184–191
S. Avidan (2005) Ensemble tracking. In Proc IEEE Comput Vis Pattern Recognit Conf. Cambridge, MA 2:494–501
Black MJ, Fleet DJ, Yacoob Y (1998) A framework for modeling appearance change in image sequences. Proc. IEEE Int Conf Comput Vis pp. 660–667
Camplani M, Roberto del Blanco C, Salgado L, Jaureguizar F, Garcia N. Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers”, Pattern Recognition Letters, [pdf] [bibtex] [The final publication is available at http://www.sciencedirect.com]
Camplani M, Salgado L (2014) Background Foreground segmentation with RGB-D Kinect data: an efficient combination of classifiers. Journal of Visual Communication and Image Representation 25(1):122–136
CAVIAR. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The PASCAL Vis Object Class Chall 2010 (VOC2010) Results
Jepson AD, Fleet DJ, El-Maraghi TF (2003) Robust online appearance models for visual tracking. PAMI 25(10):1296–1311
Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. Proc IEEE Conf Comput Vis Pattern Recog
Kwon J, Lee KM (2010) Visual tracking decomposition. Proc IEEE Conf Comput Vis Pattern Recognit 1269–1276
Marco R, Gustavo M, Cristian B (2014) Day and night at the museum: intangible computer interfaces for public exhibitions. Multimedia Tools Appl 69(3):1131–1157
Mei X, Ling H (2009) Robust visual tracking using L1 minimization. Proc IEEE Int Conf Comput Vis 1436–1443
Roccetti M, Marifia G, Semeraro A (2012) Playing into the wild: a gesture-based interface for gaming in pubilc spaces. Journal of Visual Communication and Image Representation, Elsevier 23(3):426–440
Ross D, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. International Journal of Computer Vision 77(1–3):125–141
Wang Q, Chen F, Xu W, Yang M-H (2012) Object tracking via partial least squares analysis. IEEE Trans Image Proc 21(10)
Wang Q, Chen F, Yang J, Xu W, Yang M-H (2012) Transferring visual prior for online object tracking. IEEE Trans Image Proc 21:(7)
Wang S, Lu H, Yang F, Yang M-H (2011) Superpixel tracking. In Proc IEEE Int Conf Comput Vis 1323–1330
Wang D, Lu H, Yang M-H (2013) Online object tracking with sparse prototypes. TIP 22(1):314–325
Wang D, Lu H, Yang M-H (2013) Least soft-threshold squares tracking. In Proc IEEE Conf Comput Vis Pattern Recog 2371–2738
Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Local-constrained linear coding for image classification. In Proc IEEE Conf Comput Vis Pattern Recognit 3360–3367
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In Proc IEEE Conf Comput Vis Pattern Recog 1794–1801
Zhang K, Zhang L, M-H Yang (2010) Real-time compressive tracking. In Proc ECCV 864–877
Zhong W, Lu H, Yang M-H (2012) Robust object tracking via sparsity-based collaborative model. In CVPR 1838–1845
Zhong W, Lu H, Yang M-H (2014) Robust object tracking via sparse collaborative appearance model. In IEEE Transaction on Image Processing 23(5):2356–2368
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zha, Y., Cao, T., Huang, H. et al. Robust object tracking via local constrained and online weighted. Multimed Tools Appl 75, 6481–6503 (2016). https://doi.org/10.1007/s11042-015-2584-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2584-7