Dim Targets Detection and Tracking by Self-adaptive Segmentation and Particle Filter in Starry Images

Wen-Kang  Deng; Zong-Xi  Song; Wei  Gao; Fei-Peng  Li; Yin-Long  Qi; Chen-Chen  Wang

doi:10.6180/jase.2017.20.2.06

Dim Targets Detection and Tracking by Self-adaptive Segmentation and Particle Filter in Starry Images

Computer Science and Information Engineering

Wen-Kang Deng This email address is being protected from spambots. You need JavaScript enabled to view it.^1,2, Zong-Xi Song¹ , Wei Gao¹ , Fei-Peng Li^1,2, Yin-Long Qi^1,2 and Chen-Chen Wang^1,2

¹Space Optics Laboratory, Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, P.R. China
²University of Chinese Academy of Sciences, Beijing 100049, P.R. China

Received: June 27, 2016
Accepted: January 12, 2017
Publication Date: June 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.2.06

ABSTRACT

An effective method for dim and small multi-targets detection and tracking through successive CCD images in complex starry background is put forward in this paper. Optical starry background images contain a lot of interference noise besides the moving targets. Firstly, self-adaptive threshold segmentation can play an important role in eliminating noise and improving detection rate. Furthermore, back neighborhood frame correlation (BNFC) is proposed to detect and locate the target, which is sheltered by bigger interfered stars. After detection framework acquiring the location of moving targets, particle filter which has nonlinear filtering feature is applied to track the trajectories for multi-targets in real-time. Experimental results show that by using the adaptive target detection and improved particle filter, the trajectories could be achieved at a relative low signal to noise ratio (SNR 3.5) in the case of multi-targets detection and tracking in real time. The method has good prospect for engineering application.

Keywords: Complex Starry Background, Dim and Small Targets, Self-adaptive Target Detection, Particle Filter

REFERENCES

[1] Zhu, Y., Hu, W., Zhou, J., Duan, F., Sun, J. and Jiang, L., “A New Starry Images Matching Method in Dim and Small Space Target Detection,” In Image and Graphics, 2009. ICIG’09. Fifth International Conference on 2009 Sep. 2023 (pp. 447450). IEEE. doi: 10. 1109/ICIG.2009.110
[2] Hu, J., Yu, Y. and Liu, F., “Small and Dim Target Detection by Background Estimation,” Infrared Physics & Technology, Vol. 73, pp. 141148 (2015). doi: 10. 1016/j.infrared.2015.09.009
[3] Gong, Z. Z., Xu, K. B., Mu, Y. Q. and Cao, Y., “The Space Debris Environment and the Active Debris Removal Techniques,” Spacecraft Environment Engineering, Vol. 31, No. 2, pp. 129135 (2014).
[4] Wang, X. W., Wang, C. H., Zhang, Y. Y. and Xu, J. S., “Detection of Small Space Target by Dynamic Programming,” Optics and Precision Engineering, Vol. 18, No. 2, pp. 477484 (2010).
[5] Patra, J. C., Widjaja, F., Das, A. and Ang, E. L., “A Fast Neural Network-based Detection and Tracking of Dim Moving Targets in FLIR Imagery,” In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. 2005 Jul 31 (Vol. 5, pp. 3144 3149). IEEE. doi: 10.1109/IJCNN.2005.1556430
[6] Blostein, S. D. and Huang, T. S., “Detecting Small, Moving Objects in Image Sequences Using Sequential Hypothesis Testing,” IEEE Transactions on Signal Processing, Vol. 39, No. 7, pp. 16111629 (1991). doi: 10.1109/78.134399
[7] Qin, J., Chen, Q. and Qian, W. X., “A Detection Algorithm for Dim and Small Infrared Target Based on the Optical Flow Estimation and the Adaptive Background Suppression,” Acta Photonica Sinica, Vol. 3, pp. 476 482 (2011). doi: 10.3788/gzxb20114003.0476
[8] Le, C., Zhenghua, L. and Sentang, W., “Tracking of Infrared Radiation Dim Target Based on Mean-shift and Particle Filter,” In Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese 2014 Aug. 810 (pp. 671675). IEEE. doi: 10.1109/CGNCC. 2014.7007295
[9] Zhao, Y. F., Chen, L. D. and Meng Q. L., “New Algorithm for Improving Infrared Theodolite Tracking Precision for Dim Target,” Journal of Zhejiang University (Engineering Science), Vol. 7, p. 015 (2008).
[10] Tursun, D., Guiying, X. and Hamdulla, A., “A Particle Filter Based Algorithm for State Estimation of Dim Moving Point Target in IR Image Sequence,” In Intelligent Information Technology Application, 2008. IITA’08. Second International Symposium on 2008 Dec. 2022 (Vol. 2, pp. 127131). IEEE. doi: 10.1106/ IITA.2008.178
[11] Cuevas, E. V., Zaldivar, D. and Rojas, R., “Particle Filter in Vision Tracking,” e-Gnosis, Vol. 5, Art. 1 (2007).
[12] Boyle, W. S. and Smith, G. E., “The Inception of Charge-coupled Devices,” IEEE Transactions on Electron Devices, Vol. 23, No. 7, pp. 661663 (1976). doi: 10.1109/T-ED.1976.18470
[13] Savakis, A. E. and Trussell, H. J., “On the Accuracy of PSF Representation in Image Restoration,” IEEE Transactions on Image Processing, Vol. 2, No. 2, pp. 252259 (1993). doi: 10.1109/83.217229
[14] Gandhi, T., Yang, M. T., Kasturi, R., Camps, O. I., Coraor, L. D. and McCandless, J., “Performance Characterization of the Dynamic Programming Obstacle Detection Algorithm,” IEEE Transactions on Image Processing, Vol. 15, No. 5, pp. 12021214 (2006). doi: 10.1109/TIP.2005.863973