Abstract
Adaptively learning the difference between object and background, discriminative trackers are able to overcome the complex background problem in visual object tracking. However, they are not robust enough to handle the out-of-plane rotation of object, which reduces recall performance. Meanwhile, allowing individual parts certain criterion of freedom, part-based trackers can better handle the out-of-plane rotation problem. However, they are prone to be affected by complex background, leading to low precision performance. To simultaneously address both issues, we propose a collaborative strategy that makes mutual enhancement between a discriminative tracker and a part-based tracker possible to obtain better overall performance. On one hand, we use validated results from the part-based tracker to update the discriminative tracker for recall performance improvement. On the other hand, based on confident results from the discriminative tracker we adaptively update the part-based tracker for simultaneous precision performance improvement. Experiments on various challenge sequences show that our approach achieved the state-of-the-art performance, which demonstrated the effectiveness of mutual collaboration between the two trackers.
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References
Adam A, Rivlin E, Shimshoni I (2006) Robust fragments based tracking using the integral histogram. In CVPR 1:798–805
Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26:1064–1072
Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271
Babenko B, Yang MH, Belongie S (2009) Visual tracking with online multiple instance learning. In CVPR
Bradski GR (1998) Computer vision face tracking for use in a perceptual user interface. Intel Technol J 2(2):12–21
Calonder M, Lepetit V, Ozuysal M, Trzcinski T, Strecha C, Fua P (2012) BRIEF: Computing a local binary descriptor very fast. Pattern Analysis and Machine Intelligence 34(7):1281–1298
Comaniciu D, Ramesh V, Meer P (2003) Kernel-Based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5):564–577
Hare S, Saffari A, Torr PHS (2011) Struck: Structured output tracking with kernels. In ICCV, IEEE International Conference on, 263–270
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Computer Vision and Pattern Recognition. https://arxiv.org/abs/1512.03385
Kalal Z, Mikolajczyk K and Matas J (2010) Forward-backward error: automatic detection of tracking failures. In: Proceedings of the 2010 20th International Conference on Pattern Recognition. IEEE Computer Society Washington, p. 2756–2759
Kalal Z, Matas J, Mikolajczyk K (2010) P-N learning: bootstrapping binary classifiers by structural constraints. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition, CVPR, June 13 -18, San Francisco, CA, USA. http://cmp.felk.cvut.cz/~matas/papers/kalal-pn_learning-cvpr10.pdf
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. TPAMI 34(7):1409–1422
Klein DA, Schulz D, Frintrop S, Cremers AB (2010) Adaptive real-time video-tracking for arbitrary objects. IEEE/RSJ 6219(1):772–777
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In NIPS 25(2):1097–1105
Kwon J, Lee KM (2009) Tracking of a non-rigid object via patch-based sampling. In CVPR
LeCun Y, Boser B, Denker J, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to hand-written zip code recognition. Neural Computation 1(4):541–551
Lepetit V, Lagger P, Fua P (2005) Randomized trees for real-time keypointrecognition. In CVPR. doi: 10.1109/CVPR.2005.288
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Cheng-Yang F, Berg AC (2016) SSD: single shot MultiBox detector. In ECCV doi: 10.1007/978-3-319-46448-0_2
Lowe DG (2004) Distinctive image features for scale-invariant Keypoints. IJCV 60(2):91–110
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In IJCAI
Ma C, Huang JB, Yang X, Yang MH (2015) Hierarchical convolutional features for visual tracking. In CVPR
Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In CVPR
Nebehay G, Pflugfelder R (2014) Consensus-based matching and tracking of keypoints. In TPAMI, 27(10) doi: 10.1109/WACV.2014.6836013
Nebehay G, Pflugfelder R (2015) Clustering of static-adaptive correspondences for deformable object tracking. In CVPR
Nebehay G, Micusik B, Picus C, Pflugfelder R (2011) Evaluation of an online learning approach for robust object tracking. Technical Report AIT-DSS-TR-0279 AIT Austrian Institute of Technology
Ozuysal M, Fua P, Lepetit V (2007) Fast keypoint recognition in ten lines of code. In CVPR
Pernici F, Del Bimbo A (2014) Object tracking by oversampling local features. In TPAMI, 36(12)
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In CVPR
Rosten E, Porter R, Drummond T (2010) Faster and better: a machine learning approach to corner detection. Pattern Anal Mach Intell 32(1):105–110
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Vision and Pattern Recognition. https://arxiv.org/abs/1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7-12 June 2015, doi: 10.1109/CVPR.2015.7298594
Tu F, Ge SS Suryadi HP, et al (2016) Collaborative visual object tracking via hierarchical structure. In Social Robotics
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In CVPR
Vojir T, Matas J (2014) The enhanced flock of trackers. In RRIV
Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In ICCV
Wang N, Shi J, Yeung D, Jia J (2015) Understanding and diagnosing visual tracking systems. In ICCV
Wen L, Cai Z, Lei Z (2014) Robust online learned Spatio-Temporal Context model for visual tracking. IEEE Trans Image Process 23:2
Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In CVPR
Xu R, Wunsch D (2005) Survey of clustering algorithms. TNN, 16(3)
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In ECCV
Zhang K, Zhang L, Liu Q, Zhang D, Yang M-H (2014) Fast tracking via dense spatio-temporal context learning. In ECCV
Zhong W, Lu H, Yang M-H (2012) Robust object tracking via sparse collaborative appearance model. In CVPR
Acknowledgements
This work was supported by Xi’an Polytechnic University Innovation Fund for Graduate Students (CX201622) and Beijing Qfeel Technology Co., Ltd., China. We gratefully acknowledge Intel Labs China for technical support.
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Yang, Y., Chen, N. & Jiang, S. Collaborative strategy for visual object tracking. Multimed Tools Appl 77, 7283–7303 (2018). https://doi.org/10.1007/s11042-017-4633-x
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DOI: https://doi.org/10.1007/s11042-017-4633-x