Abstract
In visual tracking, how to select a suitable motion model is an important problem to deal with, since the movements in real world are always irregular in most cases. We propose a self-tuning motion model for target tracking in this paper, where the current motion model is computed according to the relative distance of the target positions in the last two frames. Our method has achieved excellent performance when experimenting on the sequences where the targets move unstably, abruptly or even when partial occlusion exists, and the method is particularly robust to the unsuitable initial motion model.
This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61175096.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li, M., Chen, W., Huang, K., et al.: Visual tracking via incremental self-tuning particle filtering on the affine group. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1315–1322. IEEE (2010)
Qie, Z., Li, J., Zhang, Y.: Adaptive particle swarm optimization-based particle filter for tracking maneuvering object. In: 2014 33rd Chinese Control Conference (CCC), pp. 4685–4690. IEEE (2014)
Wang, L., Ouyang, W., Wang, X., et al.: Visual tracking with fully convolutional networks. In: International Conference on Computer Vision (2015)
Yang, F., Lu, H., Yang, M., et al.: Robust superpixel tracking. IEEE Trans. Image Process. 23(4), 1639–1651 (2014)
Pan, J., Lim, J., Su, Z., et al.: L0-regularized object representation for visual tracking. In: British Machine Vision Conference (2014)
Ma, C., Huang, J., Yang, X., et al.: Hierarchical convolutional features for visual tracking. In: International Conference on Computer Vision (2015)
Yoon, J.H., Yang, M., Yoon, K., et al.: Interacting multiview tracker. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 903–917 (2016)
Wang, N., Shi, J., Yeung, D., et al.: Understanding and diagnosing visual tracking systems. In: International Conference on Computer Vision (2015). Kwon, J., Lee, K.M.: Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1269–1276. June (2010)
Ross, D.A., Lim, J., Lin, R., et al.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77, 125–141 (2008)
Hare, S., Saffari, A., Torr, P.H., et al.: Struck: structured output tracking with kernels. In: International Conference on Computer Vision (2011)
Zhuang, B., Lu, H., Xiao, Z., et al.: Visual tracking via discriminative sparse similarity map. IEEE Trans. Image Process. 23(4), 1872–1881 (2014)
Yilmaz, A., Javed, O., Shah, M., et al.: Object tracking: a survey. ACM Comput. Surv. 38(4) (2006)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1269–1276, June 2010
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002). doi:10.1007/3-540-47969-4_44
Alper, Y., Omar, J., Mubarak, S.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tan, H., Zhao, Q., Wang, X. (2017). Self-tuning Motion Model for Visual Tracking. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-5230-9_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5229-3
Online ISBN: 978-981-10-5230-9
eBook Packages: Computer ScienceComputer Science (R0)