Abstract
Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bay, H., Ess, A., Tuytelaars, T., et al., 2008. Speeded-up robust features (SURF). Comput. Vis. Image Understand., 110(3): 346–359. http://dx.doi.org/10.1016/j.cviu.2007.09.014
Brox, T., Bruhn, A., Papenberg, N., et al., 2004. High accuracy optical flow estimation based on a theory for warping. European Conf. on Computer Vision, p.25–36. http://dx.doi.org/10.1007/978-3-540-24673-2_3
Cheng, C.W., Ou, W.L., Fan, C.P., 2016. Fast ellipse fitting based pupil tracking design for human-computer interaction applications. IEEE Int. Conf. on Consumer Electronics, p.445–446. http://dx.doi.org/10.1109/ICCE.2016.7430685
Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.886–893. http://dx.doi.org/10.1109/CVPR.2005.177
Elhamod, M., Levine, M.D., 2013. Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans. Intell. Transp. Syst., 14(2): 688–699. http://dx.doi.org/10.1109/TITS.2012.2228640
Elmenreich, W., Koplin, M.A., 2011. Time-triggered object tracking subsystem for advanced driver assistance systems. Elektrotechn. Inform., 128(6): 203–208. http://dx.doi.org/10.1007/s00502-011-0004-x
Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing (2nd Ed.). Prentice Hall, Inc., New Jersey.
Harris, C., Stephens, M., 1988. A combined corner and edge detector. Proc. Alvey Vision Conf., p.147–151. http://dx.doi.org/10.5244/C.2.23
Henriques, J.F., Caseiro, R., Martins, P., et al., 2015. High-speed tracking with kernelized correlation filters. IEEE Trans. Patt. Anal. Mach. Intell., 37(3): 583–596. http://dx.doi.org/10.1109/TPAMI.2014.2345390
Jeong, J.M., Yoon, T.S., Park, J.B., 2014. Kalman filter based multiple objects detection-tracking algorithm robust to occlusion. Proc. SICE Annual Conf., p.941–946. http://dx.doi.org/10.1109/SICE.2014.6935235
Jia, C.X., Wang, Z.L., Wu, X., et al., 2015. A trackinglearning-detection (TLD) method with local binary pattern improved. IEEE Int. Conf. on Robotics and Biomimetics, p.1625–1630. http://dx.doi.org/10.1109/ROBIO.2015.7419004
Jung, Y., Yoon, Y., 2015. Behavior tracking model in dynamic situation using the risk ratio EM. Int. Conf. on Information Networking, p.444–448. http://dx.doi.org/10.1109/ICOIN.2015.7057942
Kalal, Z., Mikolajczyk, K., Matas, J., 2010a. Forwardbackward error: automatic detection of tracking failures. 20th Int. Conf. on Pattern Recognition, p.23–26. http://dx.doi.org/10.1109/ICPR.2010.675
Kalal, Z., Matas, J., Mikolajczyk, K., 2010b. P-N learning: bootstrapping binary classifiers by structural constraints. IEEE Conf. on Computer Vision and Pattern Recognition, 49–56. http://dx.doi.org/10.1109/CVPR.2010.5540231
Kalal, Z., Mikolajczyk, K., Matas, J., 2012. Trackinglearning-detection. IEEE Trans. Patt. Anal. Mach. Intell., 34(7): 1409–1422. http://dx.doi.org/10.1109/TPAMI.2011.239
Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. J. Basic Eng., 82(1): 35–45. http://dx.doi.org/10.1115/1.3662552
Kaur, H., Sahambi, J.S., 2015. Vehicle tracking using fractional order Kalman filter for non-linear system. Int. Conf. on Computing, Communication and Automation, p.474–479. http://dx.doi.org/10.1109/CCAA.2015.7148423
Kong, H., Akakin, H.C., Sarma, S.E., 2013. A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans. Cybern., 43(6): 1719–1733. http://dx.doi.org/10.1109/TSMCB.2012.2228639
Li, Y., Zhu, J.K., Hoi, S.C.H., 2015. Reliable patch trackers: robust visual tracking by exploiting reliable patches. IEEE Conf. on Computer Vision and Pattern Recognition, p.353–361. http://dx.doi.org/10.1109/CVPR.2015.7298632
Liu, S., Zhang, T.Z., Cao, X.C., et al., 2016. Structural correlation filter for robust visual tracking. IEEE Conf. on Computer Vision and Pattern Recognition, p.4312–4320. http://dx.doi.org/10.1109/CVPR.2016.467
Liu, T., Wang, G., Yang, Q.X., 2015. Real-time part-based visual tracking via adaptive correlation filters. IEEE Conf. on Computer Vision and Pattern Recognition, p.4902–4912. http://dx.doi.org/10.1109/CVPR.2015.7299124
Lowe, D.G., 2004. Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vis., 60(2): 91–110. http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94
Ning, G.H., Zhang, Z., Huang, C., et al., 2016. Spatially supervised recurrent convolutional neural networks for visual object tracking. arXiv:1607.05781v1.
Prakash, U.M., Thamaraiselvi, V.G., 2014. Detecting and tracking of multiple moving objects for intelligent video surveillance systems. 2nd Int. Conf. on Current Trends in Engineering and Technology, p.253–257. http://dx.doi.org/10.1109/ICCTET.2014.6966297
Redmon, J., Divvala, S., Girshick, R., et al., 2016. You only look once: unified, real-time object detection. IEEE Conf. on Computer Vision and Pattern Recognition, p.779–788. http://dx.doi.org/10.1109/CVPR.2016.91
Sun, X., Yao, H.X., Zhang, S.P., 2010. A refined particle filter method for contour tracking. SPIE, 7744:77441M. http://dx.doi.org/10.1117/12.863450
Tarkov, M.S., Dubynin, S.V., 2013. Real-time object tracking by CUDA-accelerated neural network. J. Comput. Sci. Appl., 1(1): 1–4. http://dx.doi.org/10.12691/jcsa-1-1-1
Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.511–518. http://dx.doi.org/10.1109/CVPR.2001.990517
Xu, F., Gao, M., 2010. Human detection and tracking based on HOG and particle filter. 3rd Int. Congress on Image and Signal Processing, p.1503–1507. http://dx.doi.org/10.1109/CISP.2010.5646273
Yu, H.M., Zeng, X., 2015. Visual tracking combined with ranking vector SVM. J. Zhejiang Univ. (Eng. Sci.), 49(6): 1015–1021 (in Chinese). http://dx.doi.org/10.3785/j.issn.1008-973X.2015.06.003
Yu, W.S., Tian, X.H., Hou, Z.Q., et al., 2015. Multi-scale mean shift tracking. IET Comput. Vis., 9(1): 110–123. http://dx.doi.org/10.1049/iet-cvi.2014.0077
Zhang, R.F., Xiao, H.H., Deng, T., et al., 2016. A robust point detection algorithm based on wavelet transform for visual tracking. Int. Congress on Image and Signal Processing, Biomedical Engineering and Informatics, p.1–5. http://dx.doi.org/10.1109/CISP-BMEI.2016.7852672
Acknowledgments
The authors thank Professor Sheng-ming JIANG for his good advice.
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 61671213 and 61302058) and the Guangzhou Key Lab of Body Data Science (No. 201605030011)
A preliminary version was presented at the 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Oct. 15–17, 2016, Datong, China
ORCID: Ting DENG, http://orcid.org/0000-0001-9394-5430
Rights and permissions
About this article
Cite this article
Zhang, Rf., Deng, T., Wang, Gh. et al. A robust object tracking framework based on a reliable point assignment algorithm. Frontiers Inf Technol Electronic Eng 18, 545–558 (2017). https://doi.org/10.1631/FITEE.1601464
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1601464
Key words
- Local maximal wavelet coefficients
- Reliable point assignment
- Object tracking
- Tracking learning detection (TLD)
- Kalman filter