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Erschienen in: Machine Vision and Applications 3-4/2017

01.04.2017 | Original Paper

Multiscale salient region-based visual tracking

verfasst von: Sihua Yi, Wenyu Liu

Erschienen in: Machine Vision and Applications | Ausgabe 3-4/2017

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Abstract

This paper proposes a novel visual model to detect the salient regions of the target in complex tracking scenarios. The main idea of the proposed visual model is to generate an overcomplete set of local image patches to describe the multiscale regions of the target, and select the most important and reliable regions. The importance of each patch is evaluated by its stability and discrimination in the local feature space, while the reliability is measured by the contrast of the target and its surrounding background in the global feature space. By combining the importance and reliability, the salient regions are selected from the patch set to represent the target. Experimental results on benchmark video sequences show that the proposed visual model can improve the tracking performance effectively.

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Literatur
1.
Zurück zum Zitat Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 798–805. IEEE (2006) Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 798–805. IEEE (2006)
2.
Zurück zum Zitat Yang, M., Yuan, J., Wu, Y.: Spatial selection for attentional visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE, pp. 1–8 (2007) Yang, M., Yuan, J., Wu, Y.: Spatial selection for attentional visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE, pp. 1–8 (2007)
3.
Zurück zum Zitat Fan, J., Wu, Y., Dai, S.: Discriminative spatial attention for robust tracking. In: Computer Vision—ECCV 2010, pp. 480–493. Springer (2010) Fan, J., Wu, Y., Dai, S.: Discriminative spatial attention for robust tracking. In: Computer Vision—ECCV 2010, pp. 480–493. Springer (2010)
4.
Zurück zum Zitat Cehovin, L., Kristan, M., Leonardis, A.: Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 941–953 (2013)CrossRef Cehovin, L., Kristan, M., Leonardis, A.: Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 941–953 (2013)CrossRef
5.
Zurück zum Zitat Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26(1), 63–84 (1998)CrossRef Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26(1), 63–84 (1998)CrossRef
6.
Zurück zum Zitat Isard, M., Blake, A.: Condensationconditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRef Isard, M., Blake, A.: Condensationconditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRef
7.
Zurück zum Zitat Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRef Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRef
8.
Zurück zum Zitat Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)CrossRef Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)CrossRef
9.
Zurück zum Zitat Yao, Z., Liu, W.: Extracting robust distribution using adaptive gaussian mixture model and online feature selection. Neurocomputing 101, 258–274 (2013)CrossRef Yao, Z., Liu, W.: Extracting robust distribution using adaptive gaussian mixture model and online feature selection. Neurocomputing 101, 258–274 (2013)CrossRef
10.
Zurück zum Zitat Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443. IEEE (2009) Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)
11.
Zurück zum Zitat Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837. IEEE (2012) Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837. IEEE (2012)
12.
Zurück zum Zitat Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2042–2049. IEEE (2012) Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2042–2049. IEEE (2012)
13.
Zurück zum Zitat Lim, J., Ross, D.A., Lin, R.-S., Yang, M.-H.: Incremental learning for visual tracking. In: Advances in Neural Information Processing Systems (NIPS), vol. 17, pp. 793–800. Vancouver, British Columbia, Canada (2004) Lim, J., Ross, D.A., Lin, R.-S., Yang, M.-H.: Incremental learning for visual tracking. In: Advances in Neural Information Processing Systems (NIPS), vol. 17, pp. 793–800. Vancouver, British Columbia, Canada (2004)
14.
Zurück zum Zitat Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)CrossRef Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)CrossRef
15.
Zurück zum Zitat Zhou, Y., Bai, X., Liu, W., Latecki, L.J.: Fusion with diffusion for robust visual tracking. In: Advances in Neural Information Processing Systems (NIPS), vol. 25, pp. 2978–2986. Lake Tahoe, Harrahs and Harveys, USA (2012) Zhou, Y., Bai, X., Liu, W., Latecki, L.J.: Fusion with diffusion for robust visual tracking. In: Advances in Neural Information Processing Systems (NIPS), vol. 25, pp. 2978–2986. Lake Tahoe, Harrahs and Harveys, USA (2012)
16.
Zurück zum Zitat Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Computer Vision—ECCV 2012, pp. 702–715. Springer (2012) Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Computer Vision—ECCV 2012, pp. 702–715. Springer (2012)
17.
Zurück zum Zitat Zhang, K., Zhang, L., Yang, M.-H., Zhang, D.: Fast tracking via spatio-temporal context learning. arXiv preprint arXiv:1311.1939 Zhang, K., Zhang, L., Yang, M.-H., Zhang, D.: Fast tracking via spatio-temporal context learning. arXiv preprint arXiv:​1311.​1939
18.
Zurück zum Zitat Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1208–1215. IEEE (2009) Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1208–1215. IEEE (2009)
19.
Zurück zum Zitat Kwon, J., Lee, K.M.: Highly nonrigid object tracking via patch-based dynamic appearance modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2427–2441 (2013)MathSciNetCrossRef Kwon, J., Lee, K.M.: Highly nonrigid object tracking via patch-based dynamic appearance modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2427–2441 (2013)MathSciNetCrossRef
20.
Zurück zum Zitat Li, Y., Zhu, J., Hoi, S.C.H.: Reliable patch trackers: robust visual tracking by exploiting reliable patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 353–361 (2015). doi:10.1109/CVPR.2015.7298632 Li, Y., Zhu, J., Hoi, S.C.H.: Reliable patch trackers: robust visual tracking by exploiting reliable patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 353–361 (2015). doi:10.​1109/​CVPR.​2015.​7298632
21.
Zurück zum Zitat Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Computer Vision—ECCV 2012, pp. 864–877. Springer (2012) Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Computer Vision—ECCV 2012, pp. 864–877. Springer (2012)
22.
Zurück zum Zitat Zhang, K., Zhang, L., Yang, M.-H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)CrossRef Zhang, K., Zhang, L., Yang, M.-H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)CrossRef
23.
Zurück zum Zitat Tsagkatakis, G., Savakis, A.: Online distance metric learning for object tracking. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1810–1821 (2011) Tsagkatakis, G., Savakis, A.: Online distance metric learning for object tracking. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1810–1821 (2011)
24.
Zurück zum Zitat Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking, In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1910–1917. IEEE (2012) Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking, In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1910–1917. IEEE (2012)
25.
Zurück zum Zitat Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 983–990. IEEE (2009) Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 983–990. IEEE (2009)
26.
Zurück zum Zitat Wang, Q., Chen, F., Xu, W., Yang, M.-H.: Object tracking via partial least squares analysis. IEEE Trans. Image Process. 21(10), 4454–4465 (2012)MathSciNetCrossRef Wang, Q., Chen, F., Xu, W., Yang, M.-H.: Object tracking via partial least squares analysis. IEEE Trans. Image Process. 21(10), 4454–4465 (2012)MathSciNetCrossRef
27.
Zurück zum Zitat Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 263–270. IEEE (2011) Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 263–270. IEEE (2011)
28.
Zurück zum Zitat Zhang, T., Liu, S., Ahuja, N.: Robust visual tracking via consistent low-rank sparse learning. Int. J. Comput. Vis. 111(2), 171–190 (2015)CrossRef Zhang, T., Liu, S., Ahuja, N.: Robust visual tracking via consistent low-rank sparse learning. Int. J. Comput. Vis. 111(2), 171–190 (2015)CrossRef
29.
Zurück zum Zitat Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418. IEEE (2013) Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418. IEEE (2013)
Metadaten
Titel
Multiscale salient region-based visual tracking
verfasst von
Sihua Yi
Wenyu Liu
Publikationsdatum
01.04.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 3-4/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0836-4

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