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Global-patch-hybrid template-based arbitrary object tracking with integral channel features

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Abstract

Arbitrary object tracking is a challenging task in computer vision, as many factors affecting the target representation must be considered. A target template based on only the global appearance or on only the local appearance is unable to capture the discriminating information required for the robust performance of a tracker. In this paper, the target appearance is represented using a hybrid of global and local appearances along with a framework to exploit the Integral Channel Features (ICF). The proposed hybrid approach achieves fusion of the conventional global and patch-based approaches for target representation to synergize the advantages of both approaches. The ICF approach under the hybrid approach integrates heterogeneous sources of information of the target and provides feature strength to the hybrid template. The use of ICF also expedites the extraction of the structural and color features from video frames as the features are collected over multiple channels. The target appearance representation is updated based on only samples with appearances similar to the target appearance using clustering and vector quantization. These factors offer the proposed algorithm robustness to occlusion, illumination changes, and in-plane rotation. Further experimentation analyzes the effects of a change in the scale of the bounding box on the tracking performance of the proposed algorithm. The proposed approach outperforms all the state-of-the-art algorithms in all considered scenarios.

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References

  1. Smeulders AW, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. Briechle K, Hanebeck UD (2001) Template matching using fast normalized cross correlation 95–102

  4. Xue M, Haibin L (2009) Robust visual tracking using l1 minimization. In: IEEE international conference on computer vision (ICCV). Kyoto, Japan, pp 1436–1443

  5. Xue M, Haibin L, Yi W, Blasch E, Li B (2011) Minimum error bounded efficient l1 tracker with occlusion detection. In: IEEE conference on computer vision and pattern recognition (CVPR). Colorado, pp 1257–1264

  6. Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints. In: IEEE conference on computer vision and pattern recognition (CVPR). San Francisco, pp 49–56

  7. Wang Q, Chen F, Xu W, Yang M-H (2012) Object tracking via partial least squares analysis. IEEE Trans Image Process 21(10):4454–4465

    Article  MathSciNet  MATH  Google Scholar 

  8. Zhou T, He X, Xie K, Fu K, Zhang J, Yang J (2015) Robust visual tracking via efficient manifold ranking with low-dimensional compressive features. Pattern Recogn 48(8):2459–2473

    Article  Google Scholar 

  9. Yuan Y, Huan Y, Yuming F, Weisi L (2015) Visual object tracking by structure complexity coefficients. IEEE Trans Multimed 17(8):1125–1136

    Article  Google Scholar 

  10. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: IEEE conference on computer vision and pattern recognition (CVPR). New York, pp 798–805

  11. Junseok K, Kyoung Mu L (2009) 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 (CVPR). Miami, pp 1208–1215

  12. Cehovin L, Kristan M, Leonardis A (2011) An adaptive coupled-layer visual model for robust visual tracking. In: IEEE international conference on computer vision (ICCV). Barcelona, pp 1363–1370

  13. Oron S, Bar-Hillel A, Levi D, Avidan S (2012) Locally orderless tracking. In: IEEE conference on computer vision and pattern recognition (CVPR). Rhode Island, pp 1940–1947

  14. Shu W, Huchuan L, Fan Y, Ming-Hsuan Y (2011) Superpixel tracking. In: IEEE international conference on computer vision (ICCV), Barcelona, pp 1323–1330

  15. Weiming H, Xi L, Wenhan L, Xiaoqin Z, Maybank S, Zhongfei Z (2012) Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model. IEEE Trans Pattern Anal Mach Intell 34(12):2420–2440

    Article  Google Scholar 

  16. Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: IEEE conference on computer vision and pattern recognition (CVPR). Providence, pp 1822–1829

  17. Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632

    Article  Google Scholar 

  18. Zhang K, Zhang L, Liu Q, Zhang D, Yang M-H (2014) Fast visual tracking via dense spatio-temporal context learning. In: European conference on computer vision. Zurich, pp 127–141

  19. Zhu Q, Yeh M-C, Cheng K-T, Avidan S (2006) Fast human detection using a cascade of histograms of oriented gradients. In: IEEe conference on computer vision and pattern recognition (CVPR). New York, pp 1491–1498

  20. Laptev I (2006) Improvements of object detection using boosted histograms. In: British machine vision conference (BMVC). Edinburgh, pp 949–958

  21. Dollár P, Tu Z, Perona P, Belongie S (2009) Integral channel features. In: British machine vision conference (BMVC). Londan, p 5

  22. Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  23. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-Learning-Detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Article  Google Scholar 

  24. Hare S, Saffari A, Torr PH (2011) Struck: structured output tracking with kernels. In: IEEE international conference on computer vision (ICCV), Barcelona, pp 263–270

  25. Ross D, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

    Article  Google Scholar 

  26. Jepson AD, Fleet DJ, El-Maraghi TF (2003) Robust online appearance models for visual tracking. IEEE Trans Pattern Anal Mach Intell 25(10):1296–1311

    Article  Google Scholar 

  27. Kwon J, Lee K M (2010) Visual tracking decomposition. In: IEEE conference on computer vision and pattern recognition (CVPR). San Francisco, pp 1269–1276

  28. Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271

    Article  Google Scholar 

  29. Grabner H, Bischof H (2006) On-line boosting and vision. In: IEEE conference on computer vision and pattern recognition (CVPR). New York, pp 260–267

  30. Baker S, Matthews I (2004) Lucas-Kanade 20 years on: a unifying framework. Int J Comput Vis 56 (3):221–255

    Article  Google Scholar 

  31. Shu G, Dehghan A, Oreifej O, Hand E, Shah M (2012) Part-based multiple-person tracking with partial occlusion handling. In: IEEE conference on computer vision and pattern recognition (CVPR). Rhode Island, pp 1815–1821

  32. Lin Z, Davis LS, Doermann D, DeMenthon D (2007) Hierarchical part-template matching for human detection and segmentation. In: IEEE international conference on computer vision (ICCV), Rio de Janeiro, pp 1–8

  33. Porikli F (2005) Integral histogram: a fast way to extract histograms in cartesian spaces. In: IEEE conference on computer vision and pattern recognition (CVPR). San Dieg, pp 829–836

  34. Li X R, Jilkov VP (2000) Survey of maneuvering target tracking: dynamic models. In: Proceedings of SPIE conference on signal and data processing of small targets. Orlando, pp 212–235

  35. Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition (CVPR). Portland, pp 2411–2418

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Authors and Affiliations

  1. Visvesvaraya National Institute of Technology, Nagpur, India

    Mayur Rajaram Parate, Vishal R. Satpute & Kishor M. Bhurchandi

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  1. Mayur Rajaram Parate
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  2. Vishal R. Satpute
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  3. Kishor M. Bhurchandi
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Correspondence to Mayur Rajaram Parate.

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Parate, M.R., Satpute, V.R. & Bhurchandi, K.M. Global-patch-hybrid template-based arbitrary object tracking with integral channel features. Appl Intell 48, 300–314 (2018). https://doi.org/10.1007/s10489-017-0974-4

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  • DOI: https://doi.org/10.1007/s10489-017-0974-4

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