Abstract
Robust object tracking has widespread applications in human motion analysis systems, but it is challenging due to various factors, such as occlusion, illumination variation, and complex backgrounds. In this paper, we present a novel tracking method on the basis of a constrained online dictionary learning algorithm. Some existing tracking methods cannot consider background effects and thus have weak discriminative ability. Moreover, some dictionary learning-based tracking methods directly collect target templates and background templates as positive and negative dictionaries, respectively. The main issue is that the dictionaries cannot effectively represent the target and background and handle appearance changes. Thus, a constrained online dictionary learning algorithm is proposed to obtain a discriminative dictionary, which can ensure that the proposed tracker has good discriminative ability in distinguishing targets from complex backgrounds. Experimental results show that the proposed algorithm performs favorably against other state-of-the-art methods in terms of accuracy and robustness.
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References
Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 798–805
Aharon M, Elad M, Bruckstein A (2006) k-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322
Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learning. In: CVPR. IEEE, pp 983–990
Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach. In: CVPR. IEEE, pp 1830–1837
Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591
Cheng X, Zhang Y, Cui J, Zhou L (2017) Object tracking via temporal consistency dictionary learning. IEEE Trans Syst Man Cybern Syst 47(4):628–638
Dou J, Qin Q, Tu Z (2017) Robust visual tracking based on generative and discriminative model collaboration. Multimed Tools Appl 76(14):15839–15866
Hoseinnezhad R, Vo BN, Vo BT (2013) Visual tracking in background subtracted image sequences via multi-Bernoulli filtering. IEEE Trans Signal Process 61 (2):392–397
Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints. In: CVPR. IEEE, pp 49–56
Kwon J, Lee K-M (2010) Visual tracking decomposition. In: CVPR. IEEE, pp 1269–1276
Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing. In: 2011 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 1305–1312
Mei X, Ling H (2009) Robust visual tracking using L1 minimization. In: 2009 IEEE 12th International conference on computer vision. IEEE, pp 1436–1443
Oron S, Bar-Hillel A, Levi D, Avidan S (2015) Locally orderless tracking. Int J Comput Vis 111(2):213–228
Ou W, Yuan D, Liu Q et al. (2017) Object tracking based on online representative sample selection via non-negative least square. Multimed Tools Appl 1–19
Ross D, Lim J, Lin R, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141
Taalimi A, Qi H, Khorsandi R Online multi-modal task-driven dictionary learning and robust joint sparse representation for visual tracking. In: 2015 12th IEEE International conference on advanced video and signal based surveillance (AVSS). IEEE, pp 1–6
Tang S, Zhang LF, Yan JL, Tan XW, Ding GY (2016) An online LC-KSVD based dictionary learning for multi-target tracking. In: 2016 International conference on information system and artificial intelligence (ISAI). IEEE, pp 630–633
Tropp J, Gilbert A (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666
Wang S, Fu Y (2015) Locality-constrained discriminative learning and coding. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 17–24
Wang D, Lu H, Yang MH (2013) Least soft-threshold squares tracking[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2371–2378
Wang N, Wang J, Yeung DY (2013) Online robust non-negative dictionary learning for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 657–664
Wang D, Sun W, Yu S et al. (2016) A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking. Multimed Tools Appl 75 (17):10271–10289
Xie Y, Zhang W, Li C, Lin S, Qu Y, Zhang Y (2014) Discriminative object tracking via sparse representation and online dictionary learning. IEEE Trans Cybern 44(4):539–553
Xie X, Jones M, Tam G (2017) Recognition, tracking, and optimisation[J]. Int J Comput Vis 122(3):409–410
Xing J, Gao J, Li B, Hu W, Yan S (2013) Robust object tracking with online multi-lifespan dictionary learning. In: Proceedings of the IEEE International conference on computer vision, pp 665–672
Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. In: 2010 17th IEEE International conference on image processing (ICIP). IEEE, pp 1601–1604
Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn 46(1):397–411
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: ECCV. Springer, pp 864–877
Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: CVPR. IEEE, pp 2042–2049
Zhang S, Yao H, Sun X, Lu X (2013) Sparse coding based visual tracking: review and experimental comparison. Pattern Recogn 46(7):1772–1788
Zhang T, Ghanem B, Liu S, Ahuja N (2013) Robust visual tracking via structured multi-task sparse learning. Int J Comput Vis 101(2):367–383
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Liu, N., Huo, H. & Fang, T. Robust object tracking via constrained online dictionary learning. Multimed Tools Appl 78, 3689–3703 (2019). https://doi.org/10.1007/s11042-017-5538-4
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DOI: https://doi.org/10.1007/s11042-017-5538-4