Abstract
Recently, sparse representation has been utilized in many computer vision tasks and adapted for visual tracking. Sparsity-based visual tracking is formulated as searching candidates with minimal reconstruction errors from a template subspace with sparsity constraints in the approximation coefficients. However, an intensity template is easily corrupted by noise and not robust for target tracking under a dynamic environment. The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. Further, the covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized, and its dimension is small. Although the covariance matrix lies on Riemannian manifolds, its log-transformation can be measured on a Euclidean subspace. Based on the covariance region descriptor and using the sparse representation, we propose a novel tracking approach on the Log-Euclidean Riemannian subspace. Specifically, the target region is characterized by a covariance matrix which is then log-transformed from the Riemannian manifold to the Euclidean subspace. After that, the target tracking problem is integrated under a sparse approximation framework, where the sparsity is achieved by solving an ℓ1-regularization problem. Then the candidate with the smallest approximation is taken as the tracked target. For target propagation, we use the Bayesian state inference framework, which propagates sample distributions over time using the particle filter algorithm. To evaluate our method, we have collected several video sequences and the experimental results show that our tracker can achieve robustly and reliably target tracking.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Isard, M., Blake, A.: Condensation-Conditional Density Propagation for Visual Tracking. Int’l Journal of Computer Vision 29, 5–28 (1998)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online Learning for Matrix Factorization and Sparse Coding. J. Machine Learning Research 11, 19–60 (2010)
Mei, X., Ling, H.: Robust Visual Tracking using ℓ1 Minimization. In: ICCV (2009)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE T. Pattern Analysis and Machine Intelligence 31(1), 210–227 (2009)
Wu, Y., Wu, B., Liu, J., Lu, H.Q.: Probabilistic Tracking on Riemannian Manifolds. In: ICPR (2008)
Wu, Y., Wang, J.Q., Lu, H.Q.: Robust Bayesian tracking on Riemannian manifolds via fragments-based representation. In: ICASSP (2009)
Wu, Y., Cheng, J., Wang, J., Lu, H.: Real-time Visual Tracking via Incremental Covariance Tensor Learning. In: ICCV (2009)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006)
Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. on Matrix Analysis and Applications 29(1) (2008)
Li, X., Hu, W., Zhang, Z., Zhang, X., Zhu, M., Cheng, J.: Visual tracking via incremental Log-Euclidean Riemannian subspace learning. In: CVPR (2008)
Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Int’l Journal of Computer Vision 66(1), 41–66 (2006)
Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)
Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on Lie Algebra. In: CVPR, pp. 728–735 (2006)
Donoho, D.: Compressed sensing. IEEE T. Information Theory 52(4), 1289–1306 (2006)
Ling, H., Wu, Y., Blasch, E., Chen, G., Lang, H., Bai, L.: Evaluation of Visual Tracking in Extremely Low Frame Rate Wide Area Motion Imagery. Fusion (2011)
Chen, M., Pang, S.K., Cham, T.J., Goh, A.: Visual Tracking with Generative Template Model based on Riemannian Manifold of Covariances. Fusion (2011)
Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010)
Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum Error Bounded Efficient ℓ1 Tracker with Occlusion Detection. In: CVPR (2011)
Hong, X., Chang, H., Shan, S., Chen, X., Gao, W.: Sigma set: A small second order statistical region descriptor. In: CVPR, pp. 1802–1809 (2009)
Tosato, D., Farenzena, M., Spera, M., Murino, V., Cristani, M.: Multi-class classification on Riemannian manifolds for video surveillance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 378–391. Springer, Heidelberg (2010)
Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on Riemannian manifolds. In: CVPR (2007)
Paisitkriangkrai, S., Shen, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE T. Circuits & Systems for Video Technology 18(8), 1140–1151 (2008)
Pang, Y., Yuan, Y., Li, X.: Gabor-based region covariance matrices for face recognition. IEEE T. Circuits & Systems for Video Technology 18(7), 989–993 (2008)
Guo, K., Ishwar, P., Konrad, J.: Action change detection in video by covariance matching of silhouette tunnels. In: ICASSP, pp. 1110–1113 (2010)
Candès, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. on Pure and Applied Mathematics 59(8), 1207–1223 (2006)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. Int’l Journal of Computer Vision 56, 221–255 (2004)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE T. Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Hager, G., Belhumeur, P.: Real-time tracking of image regions with changes in geometry and illumination. In: CVPR, pp. 403–410 (1996)
Wu, Y., Blasch, E., Chen, G., Bai, L., Ling, H.: Multiple Source Data Fusion via Sparse Representation for Robust Visual Tracking. Fusion (2011)
Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE T. Image Processing 11, 1491–1506 (2004)
https://www.sdms.afrl.af.mil/index.php?collection=video_sample_set_2
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Y., Ling, H., Blasch, E., Bai, L., Chen, G. (2011). Visual Tracking Based on Log-Euclidean Riemannian Sparse Representation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-24028-7_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
eBook Packages: Computer ScienceComputer Science (R0)