Accurate Segmentation for Infrared Flying Bird Tracking

ZHENG Hong; HUANG Ying; LING Haibin; ZOU Qi; YANG Hao

doi:10.1049/cje.2016.07.008

Volume 25 Issue 4

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2016 > 25(4): 625-631

ZHENG Hong, HUANG Ying, LING Haibin, et al., “Accurate Segmentation for Infrared Flying Bird Tracking,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 625-631, 2016, doi: 10.1049/cje.2016.07.008

Citation:

ZHENG Hong, HUANG Ying, LING Haibin, et al., “Accurate Segmentation for Infrared Flying Bird Tracking,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 625-631, 2016, doi: 10.1049/cje.2016.07.008

Citation:

PDF( 418 KB)

Accurate Segmentation for Infrared Flying Bird Tracking

doi: 10.1049/cje.2016.07.008

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2.
Department of Computer and Information Science, Temple University, Philadelphia 19122, USA;
3.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
4.
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Funds: This work is partly supported by the Beijing Higher Education Young Elite Teacher Project (No.YETP0546).

More Information

Corresponding author: HUANG Ying (corresponding author) received the Ph.D. degree from the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, in 2016. He was a visiting student with Temple University, Philadelphia, PA, USA, in 2014. His research interests include computer vision and machine learning. (Email:yw155@buaa.edu.cn)
Received Date: 2015-09-01
Rev Recd Date: 2016-03-23
Publish Date: 2016-07-10

Abstract

Abstract

Bird strikes present a huge risk for air vehicles, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation. For improving the effectiveness and efficiency of bird monitoring, computer vision techniques have been proposed to detect birds, determine bird flying trajectories, and predict aircraft takeoff delays. Flying bird with a huge deformation causes a great challenge to current tracking algorithms. We propose a segmentation based approach to enable tracking can adapt to the varying shape of bird. The approach works by segmenting object at a region of interest, where is determined by the object localization method and heuristic edge information. The segmentation is performed by Markov random field, which is trained by foreground and background mixture Gaussian models. Experiments demonstrate that the proposed approach provides the ability to handle large deformations and outperforms the most state-of-the-art tracker in the infrared flying bird tracking problem.
- Object tracking,
- Edge detection,
- Segmentation,
- Gaussian mixture model,
- Markov random field (MRF)

FullText(HTML)

References(28)

References

J.L. Spiesberger, "Locating animals from their sounds and tomography of the atmosphere:Experimental demonstration", Journal of the Acoustical Society of America, Vol.106, No.2, pp.837-846, 1999.

P.S. Coates, M.L. Casazza, B.J. Halstead, et al., "Using avian radar to examine relationships among avian activity, bird strikes, and meteorological factors", Human-Wildlife Interactions, Vol.5, No.2, pp.249-268, 2011.

W.W. Verstraeten, B. Vermeulen, J. Stuckens, et al., "Webcams for bird detection and monitoring:A demonstration study", Sensors, Vol.10, No.4, pp.3480-3503, 2010.

J. Zhang, Q. Xu, X. Cao, et al., "Hierarchical incorporation of shape and shape dynamics for flying bird detection", Neurocomputing, Vol.131, pp.179-190, 2014.

X. Mei, H. Ling, Y. Wu, et al., "Efficient minimum error bounded particle resampling L1 tracker with occlusion detection", IEEE Transactions on Image Processing, Vol.22, No.7, pp.2661-2675, 2013.

P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, pp.511-518, 2001.

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp.886-893, 2005.

L. Wang, Y. Liang, W. Cai, et al., "Failure detection and correction for appearance based facial tracking", Chinese Journal of Electronics, Vol.24, No.1, pp.20-25, 2015.

S. Hare, S. Golodetz, A. Saffari, et al., "Struck:Structured output tracking with kernels", IEEE Transactions on Pattern Recognition and Machine Intelligence, 2015, DOI: 10.1109/TPAMI.2015.2509974.

J. Henriques, C. Rui, P. Martins, et al., "Exploiting the circulant structure of tracking-by-detection with kernels", Proc. of the 12th European Conference on Computer Vision, Florence, Italy, pp.702-715, 2012.

M. Godec, P. Roth and H. Bischof, "Hough-based tracking of non-rigid objects", Proc. of IEEE International Conference on Computer Vision, Barcelona, Spain, pp.81-88, 2011.

S. Duffner and C. Garcia, "PixelTrack:A fast adaptive algorithm for tracking non-rigid objects", Proc. of IEEE International Conference on Computer Vision, Sydney, Australia, pp.2480-2487, 2013.

C. Rother, V. Kolmogorov and A. Blake, "GrabCut-Interactive foreground extraction using iterated graph cut", Proc. of the 31st International Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA, pp.309-314, 2004.

D. Tsai, M. Flagg, A. Nakazawa, et al., "Motion coherent tracking using multi-label MRF optimization", International Journal of Computer Vision, Vol.100, No.2, pp.190-202, 2012.

L. Wen, D. Du, Z. Lei, et al., "JOTS:Joint online tracking and segmentation", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp.2226-2234, 2015.

R. Yao, Q. Shi, C. Shen, et al., "Part-based visual tracking with online latent structural learning", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp.2363-2370, 2013.

Y. Pang and H. Ling, "Finding the best from the second bests-inhibiting subjective bias in evaluation of visual tracking algorithms", Proc. of IEEE International Conference on Computer Vision, Sydney, Australia, pp.2784-2791, 2013.

Y. Wu, J. Lim and M. Yang, "Online tracking:A benchmark", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp.2411-2418, 2013.

J. Canny, "A computational approach to edge detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.8, No.6, pp.679-698, 1986.

A.P. Dempster, N.M. Laird and D.B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm", Journal of the Royal Statistical Society, Vol.39, No.1, pp.1-38, 1977.

R. Szeliski, R. Zabih, D. Scharstein, et al., "A comparative study of energy minimization methods for Markov random fields with smoothness-based priors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.6, pp.1068-1080, 2008.

J.C. Platt, Advances in Kernel Methods, MIT Press, MA, USA, pp.185-208, 1999.

K. Zhang, L. Zhang and M. Yang, "Real-time compressive tracking", Proc. of the 12th European Conference on Computer Vision, Florence, Italy, pp.864-877, 2012.

L. Sevilla-Lara and E.G. Learned-Miller, "Distribution fields for tracking", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp.1910-1917, 2012.

X. Jia, H. Lu and M. Yang, "Visual tracking via adaptive structural local sparse appearance model", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp.1822-1829, 2012.

C. Bao, Y. Wu, H. Ling, et al., "Real time robust L1 tracker using accelerated proximal gradient approach", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp.1830-1837, 2012.

W. Zhong, H. Lu and M. Yang, "Robust object tracking via sparsity-based collaborative model", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp.1838-1845, 2012.

S. Oron, A. Bar-Hillel, D. Levi, et al., "Locally orderless tracking", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp.1940-1947, 2012.

Relative Articles

Supplements(0)

Cited By

Proportional views