Shreyamsha Kumar, B. K. ORCID: https://orcid.org/0000-0002-3781-0635, Swamy, M.N.S. ORCID: https://orcid.org/0000-0002-3989-5476 and Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 (2018) Visual tracking using structural local DCT sparse appearance model with occlusion detection. Multimedia Tools and Applications, 78 (6). pp. 7243-7266. ISSN 1380-7501
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9MBVisual Tracking using Structural Local DCT Sparse Appearance Model with Occlusion Detection.pdf - Accepted Version Available under License Spectrum Terms of Access. |
Official URL: https://doi.org/10.1007/s11042-018-6453-z
Abstract
In this paper, a structural local DCT sparse appearance model with occlusion detection is proposed for visual tracking in a particle filter framework. The energy compaction property of the 2D-DCT is exploited to reduce the size of the dictionary as well as that of the candidate samples so that the computational cost of l1-minimization can be lowered. Further, a holistic image reconstruction procedure is proposed for robust occlusion detection and used for appearance model update, thus avoiding the degradation of the appearance model in the presence of occlusion/outliers. Also, a patch occlusion ratio is introduced in the confidence score computation to enhance the tracking performance. Quantitative and qualitative performance evaluations on two popular benchmark datasets demonstrate that the proposed tracking algorithm generally outperforms several state-of-the-art methods.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
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Item Type: | Article |
Refereed: | Yes |
Authors: | Shreyamsha Kumar, B. K. and Swamy, M.N.S. and Ahmad, M. Omair |
Journal or Publication: | Multimedia Tools and Applications |
Date: | 8 August 2018 |
Funders: |
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Digital Object Identifier (DOI): | 10.1007/s11042-018-6453-z |
Keywords: | Visual tracking, Local DCT sparse appearance model, Holistic image reconstruction, Reconstruction error, Occlusion map, Observation model update |
ID Code: | 985427 |
Deposited By: | M. OMAIR AHMAD |
Deposited On: | 22 May 2019 18:06 |
Last Modified: | 22 May 2019 18:06 |
Related URLs: |
References:
1. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 798–8052. Babenko B, Yang MH, Belongie S (2009) Visual tracking with online multiple instance learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 983–990
3. Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1830–1837
4. Chen D, Liu Q, Sun M, Yang J (2008) Mining appearance models directly from compressed video. IEEE Trans Multimed 10(2):268–276
5. Chen H, Zhang W, Zhao X, Tan m (2014) DCT representations based appearance model for visual tracking. In: Proceedings of the IEEE international conference on robotics and biometrics (ROBIO), pp 1614–1619
6. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell (PAMI) 25(5):564–577
7. Dai P, Luo Y, Liu W, Li C, Xie Y (2013) Robust visual tracking via part-based sparsity model. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1803–1806
8. Danelljan M, Hager G, Khan FS, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 4310–4318
9. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image process 15(12):3736–3745
10. Gao J, Zhang T, Yang X, Xu C (2017) Deep relative tracking. IEEE Trans Image Process 26(4):1845–1858
11. Gao J, Zhang T, Yang X, Xu C (2018) P2T: Part-to-target tracking via deep regression learning. IEEE Trans Image Process 27(6):3074–3086
12. Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: Proceedings of European conference on computer vision (ECCV), pp 234–247
13. Hafed ZM, Levine MD (2001) Face recognition using the discrete cosine transform. Int J Comput Vis 43(3):167–188
14. He D, Gu Z, Cercone N (2009) Efficient image retrieval in DCT domain by hypothesis testing. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 225–228
15. Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell (PAMI) 37(3):583–596
16. Isard M, Blake A (1998) Condensation: Conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28
17. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1822–1829
18. Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R (2016) The visual object tracking VOT2016 challenge results. In: Proceedings of European conference on computer vision (ECCV), pp 1–45
19. Li Y, Ai H, Yamashita T, Lao S, Kawade M (2008) Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Pattern Anal Mach Intell (PAMI) 30(10):1728–1740
20. Li X, Dick A, Shen C, Hengel A, Wang H (2013) Incremental learning of 3d-DCT compact representations for robust visual tracking. IEEE Trans Pattern Anal Mach Intell (PAMI) 35(4):863–881
21. Li H, Li Y, Porikli F (2016) Deeptrack: Learning discriminative feature representations online for robust visual tracking. IEEE Trans Image Process 25(4):1834–1848
22. Lin C, Pun CM (2013) Tracking object using particle filter and DCT features. In: Proceedings of international conference on advances in computer science and engineering, pp 167–169
23. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60
24. Mei X, Ling H (2009) Robust visual tracking using L1 minimization. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1436–1443
25. Mei X, Ling H, Wu Y, Blasch E, Bai L (2011) Minimum error bounded efficient L1 tracker with occlusion detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1257–1264
26. Ou W, Yuan D, Liu Q, Cao Y (2018) Object tracking based on online representative sample selection via non-negative least square. Multimedia Tools Appl 77(9):10569–10587
27. Pennerbaker W, Mithchell J (1992) JPEG: Still image data compression standard. Springer Science & Business Media, Berlin
28. Qu P (2014) Visual tracking with fragments-based PCA sparse representation. Int J Signal Process, Image Process Pattern Recogn 7(2):23–34
29. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77:125–141
30. Shreyamsha Kumar BK, Swamy MNS, Omair Ahmad M (2013) Multiresolution DCT decomposition for multifocus image fusion. In: Proceedings of the IEEE Canadian conference on electrical and computer engineering (CCECE), pp 1–4. https://doi.org/10.1109/CCECE.2013.6567721
31. Shreyamsha Kumar BK, Swamy MNS, Omair Ahmad M (2015) Structural local DCT sparse appearance model for visual tracking. In: Proceedings of the IEEE international symposium on circuits and systems (ISCAS), pp 1194–1197. https://doi.org/10.1109/ISCAS.2015.7168853
32. Shreyamsha Kumar BK, Swamy MNS, Omair Ahmad M (2016) Visual tracking via bilateral 2DPCA and robust coding. In: Proceedings of the IEEE Canadian conference on electrical and computer engineering (CCECE), pp 1–4. https://doi.org/10.1109/CCECE.2016.7726647
33. Shreyamsha Kumar BK, Swamy MNS, Omair Ahmad M (2016) Weighted residual minimization in PCA subspace for visual tracking. In: Proceedings of the IEEE international symposium on circuits and systems (ISCAS), pp 986–989. https://doi.org/10.1109/ISCAS.2016.7527408
34. Uzair M, Mahmood A, Mian AS (2013) Hyperspectral face recognition using 3d-DCT and partial least squares. In: Proceedings of British machine vision conference (BMVC), pp 1–10
35. Wang D, Lu H (2012) Object tracking via 2DPCA and L1-regularization. Signal Process Lett 19(11):711–714
36. Wang D, Lu H, Bo C (2015) Fast and robust object tracking via probability continuous outlier model. IEEE Trans Image Process 24(12):5166–5176
37. Wang D, Lu H, Bo C (2015) Visual tracking via weighted local cosine similarity. IEEE Trans Cybern 45(9):1838–1850
38. Wang D, Lu H, Yang MH (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325
39. Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking. In: Proceedings of advances in neural information processing systems (NIPS), pp 809–817
40. Wang F, Zhang J, Guo Q, Liu P, Tu D (2015) Robust visual tracking via discriminative structural sparse feature. In: Proceedings of the Chinese conference on image and graphics technologies, pp 438–446
41. Wang D, Lu H, Xiao Z, Yang MH (2015) Inverse sparse tracker with a locally weighted distance metric. IEEE Trans Image Process 24(9):2646–2657
42. Wang D, Lu H, Yang MH (2016) Robust visual tracking via least soft-threshold squares. IEEE Trans Circ Syst Video Technol 26(9):1709–1721
43. Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell (PAMI) 31(2):210–227
44. Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2411–2418
45. Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1794–1801
46. Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831
47. You X, Li X, He Z, Zhang X (2015) A robust local sparse tracker with global consistency constraint. Signal Process 111:308–318
48. Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2042–2049
49. Zhang H, Tao F, Yang G (2015) Robust visual tracking based on structured sparse representation model. Multimed Tools Appl 74(3):1021–1043
50. Zhang T, Bibi A, Ghanem B (2016) In defense of sparse tracking: Circulant sparse tracker. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3880–3888
51. Zhang T, Xu C, Yang MH (2017) Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4819–4827
52. Zhang T, Liu S, Xu C, Liu B, Yang MH (2018) Correlation particle filter for visual tracking. IEEE Trans Image Process 27(6):2676–2687
53. Zhang T, Xu C, Yang MH (2018) Learning multi-task correlation particle filters for visual tracking. IEEE Trans Pattern Anal Mach Intell (PAMI):1–14. https://doi.org/10.1109/TPAMI.2018.2797062
54. Zhong Y, Zhang H, Jain AK (2000) Automatic caption localization in compressed video. IEEE Trans Pattern Anal Mach Intell (PAMI) 22(4):385–392
55. Zhuang B, Wang L, Lu H (2016) Visual tracking via shallow and deep collaborative model. Neurocomputing 218:61–71
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