Particle filter-based modulation domain infrared targets tracking
- 26 Downloads
Faced with problems of low contrast, poor SNR, and relatively complicated tracking environment, stable infrared target tracking is worth researching for its many potential applications. In this paper, instead of traditional target tracking in the pixel domain, we propose a sampling importance resampling (SIR) particle filter method with indirect velocity measurements to track infrared targets in the modulation domain. The dominant amplitude modulation (AM) features used for tracking is extracted by decomposing the input image using an 18-channel Gabor filter bank followed by the application of the dominant component analysis approach. The dominant AM modulation features provide a significant partial texture characteristic of the target which can be separated from background with better discrimination. To take advantage of observed kinematics, we utilize the augmented state vector with indirect velocity information via combining the measurements of velocity in adjacent frames to the SIR particle filter framework, which weakens weights of particles with bad velocity estimates but still having association with the cluttered background or other moving objects. A dynamic template update strategy is also provided to prevent the tracker from appearance model drift. Experiments indicate that the proposed method is effective for raising the tracking accuracy compared with other tracking methods.
KeywordsInfrared target tracking SIR particle filter Modulation domain AM features Dominant component analysis Augmented state vector
This work was supported by the National Nature Science Foundation of China (Grant No. 61701233), and China Scholarship. The author would also like to thank Professor Joseph Havlicek and Doctor Jonathan Williams of the University of Oklahoma for the great help of the research.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest concerning the content of this study.
- Bello, J.C.F., Havlicek, J.P.: A state vector augmentation technique for incorporating indirect velocity information into the likelihood function of the sir video target tracking filter. IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE (2016)Google Scholar
- Han, T.X., Ming, L., Thomas, S.H.: A drifting-proof framework for tracking and online appearance learning. IEEE Workshop on Applications of Computer Vision, WACV’07. IEEE (2007)Google Scholar
- Havlicek, J.P., Bovik, A., Chen, D.: AM–FM image modeling and Gabor analysis. Opt. Eng. N. Y. Marcel Dekker Inc. 64, 343–386 (1999)Google Scholar
- Havlicek, J.P., Tay, P.C., Bovik, A.C.: AM–FM image models: fundamental techniques and emerging trends. In: Handbook of Image and Video Processing, 2nd edn (2005)Google Scholar
- Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: IEEE International Conference on Computer Vision, DBLP, pp. 1436–1443 (2009)Google Scholar
- Nguyen, C.T., Havlicek, J.P.: Modulation domain features for discriminating infrared targets and backgrounds. In: IEEE International Conference on Image Processing. IEEE (2006)Google Scholar
- Nguyen, C.T., Havlicek, J.P., Yeary, M.: Modulation domain template tracking. IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07. IEEE (2007)Google Scholar
- Nguyen, C.T., Havlicek, J.P., Fan, G., et al.: Robust dual-band MWIR/LWIR infrared target tracking. In: Asilomar Conference on Signals, Systems and Computers, pp. 78–83. IEEE (2015)Google Scholar
- Pan, J., Bo, H.: Robust object tracking against template drift. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 3. IEEE (2007)Google Scholar
- Prakash, R.S., Aravind, R.: Modulation-domain particle filter for template tracking. In: 19th International Conference on Pattern Recognition. ICPR 2008. IEEE, 2008Google Scholar
- Wang, Z., Zhang, K.D., Wu, Y., et al.: Tracking dim target in infrared imagery using the trust region embedded particle filter. In: International Conference on Signal Processing. IEEE (2006)Google Scholar
- Wang, Q., Chen, F., Xu, W., et al.: Online discriminative object tracking with local sparse representation. IEEE Workshop on the Applications of Computer Vision. IEEE Computer Society (2012)Google Scholar
- Wang, F., Zhen, Y., Zhong, B., et al.: Robust infrared target tracking based on particle filter with embedded saliency detection. Inf. Sci. Int. J. 301(C), 215–226 (2015)Google Scholar
- Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418. IEEE Computer Society (2013)Google Scholar