Skip to main content
SpringerLink
Log in

A precise approach to tracking dim-small targets using spectral fingerprint features

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

A precise method for accurately tracking dim-small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparametric kernel density estimator is built using the probability histogram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is accurate, fast, and robust. It meets the needs of dim-small target tracking adequately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Doucet A, Gordon N, Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 2001, 49(3): 613–624

    Article  Google Scholar 

  2. Gao J, Kosaka A, Kak A. A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments. Computer Vision and Image Understanding, 2005, 99(1): 1–57

    Article  Google Scholar 

  3. Meyer M, Ohmacht T, Bosch R, Hotter M. Video surveillance applications using multiple views of a scene. IEEE Aerospace and Electronic Systems Magazine, 1999, 14(3): 13–18

    Article  Google Scholar 

  4. Wang L, Hu S, Zhang X. Detecting and tracking of small moving target under the background of sea level. In: Proceedings of the 9th International Conference on Signal Processing. 2008, 989–992

  5. Hamdulla A, Xiang G, Tursun D. A particle filter and fuzzy clustering based algorithm for tracking dim moving multiple point targets in ir image sequence. In: Proceedings of 2009 WRI World Congress on Computer Science and Information Engineering. 2009, 205–209

  6. Chen J, An G, Zhang S, Wu Z. Small target tracking based on histogram interpolation mean shift. Journal of Electronics and Information Technology, 2010, 32(9): 2119–2125

    Article  Google Scholar 

  7. Neumann J. DMD based hyperspectral augmentation for multi-object tracking systems. In: Proceedings of Emerging Digital Micromirror Device Based Systems and Applications, SPIE-7210. 2009, 110–119

  8. Varsano L, Rotman S. Point target tracking in hyperspectral images. In: Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE-5806. 2005, 503–512

  9. Varsano L, Yatskaer I, Rotman S. Temporal target tracking in hyperspectral images. Optical Engineering, 2006, 45(12): 126201

    Article  Google Scholar 

  10. Aminov B, Nichtern O, Rotman S. Spatial and temporal point tracking in real hyperspectral images. EURASIP Journal on Advances in Signal Processing, 2011, 2011(1): 1–25

    Article  Google Scholar 

  11. Rosario D, Kling H. Hyperspectral object tracking using small sample size. In: Hyperspectral, and Ultraspectral Imagery XVI, SPIE-7695. 2010, 230–238

  12. Banerjee A, Burlina P, Broadwater J. Hyperspectral video for illumination-invariant tracking. In: Proceedings of the 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. 2009, 1–4

  13. Kerekes J, Baum J. Hyperspectral imaging system modeling. Lincoln Laboratory Journal, 2003, 14(1): 117–130

    Google Scholar 

  14. Wang T, Zhu Z, Blasch E. Bio-inspired adaptive hyperspectral imaging for real-time target tracking. IEEE Sensors Journal, 2010, 10(3): 647–654

    Article  Google Scholar 

  15. Nguyen H V, Banerjee A, Chellappa R. Tracking via object reflectance using a hyperspectral video camera. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2010, 44–51

  16. Garcia-Allende P, Conde O, Mirapeix J, Cobo A, Lopez-Higuera J. Quality control of industrial processes by combining a hyperspectral sensor and Fisher’s linear discriminant analysis. Sensors and Actuators B: Chemical, 2008, 129(2): 977–984

    Article  Google Scholar 

  17. Wettle M, Daniel P, Logan G, Thankappan M. Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: a sensitivity study based on the optical properties of two different oil types and the hymap and quickbird sensors. Remote Sensing of Environment, 2009, 113(9): 2000–2010

    Article  Google Scholar 

  18. Delabrouille J, Cardoso J, Patanchon G. Multi-detector multicomponent spectral matching and applications for cmb data analysis. Monthly Notices of the Royal Astronomical Society. 2002, 1–16

  19. Blackburn J, Mendenhall M, Rice A, Shelnutt P, Soliman N, Vasquez J. Feature aided tracking with hyperspectral imagery. In: Proceedings of Signal and Data Processing of Small Targets, SPIE-6699. 2007, 1–12

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Hao Sheng, Chao Li, Yuanxin Ouyang & Zhang Xiong

  2. Research Institute of Beihang University in Shenzhen, Shenzhen, 518057, China

    Hao Sheng, Chao Li & Zhang Xiong

  3. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Yuanxin Ouyang

Authors
  1. Hao Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanxin Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Li.

Additional information

Hao Sheng Received his BS and PhD degrees from the School of Computer Science and Engineering of Beihang University in 2003 and 2009, respectively. Now he is an assistant professor at Beihang University. He is working on computer vision, intelligent transportation systems, and hyperspectral remote sensing.

Chao Li Received his BS and PhD degrees in computer science and technology from Beihang University in 1996 and 2005, respectively. Now he is an associate professor and MS supervisor in the School of Computer Science and Engineering, Beihang University. Currently, he is working on data vitalization and computer vision.

Yuanxin Ouyang Received her BS and PhD degrees in computer science and technology from Beihang University in 1996 and 2005, respectively. Now she is an associate professor and MS supervisorin in the School of Computer Science and Engineering, Beihang University. She is working on data vitalization, web applications, and distributed systems.

Zhang Xiong Received his BS degree from Harbin Engineering University in 1982. He Received his MS degree from Beihang University in 1985. He is a professor and PhD supervisor in the School of Computer Science and Engineering, Beihang University. He is working on computer vision, wireless sensor networks, information security, and data vitalization.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sheng, H., Li, C., Ouyang, Y. et al. A precise approach to tracking dim-small targets using spectral fingerprint features. Front. Comput. Sci. 6, 527–536 (2012). https://doi.org/10.1007/s11704-012-1106-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-012-1106-2

Keywords

Search

Navigation