Abstract
Aiming at the multiple targets recognition and tracking in SAR images, a robust feature extraction method and a combined recognition and tracking method for multi-class slow-moving targets based on visual cognition is presented in this paper. To obtain robust feature and high classification precision, a local multi-resolution analysis and feature extraction method based on the visual attention mechanism and a multiple kernel classifier is studied, which realizes the quick classification with high accuracy for multi-class image targets. According to the recognition result and the corresponding relationship of targets in the adjacent frames, the targets’ motion parameters are estimated utilizing the unscented Kalman filter (UKF) based on the “what” and “where” pathways information processing mechanism. As a result, the high performance tracking of multi-class slow-moving targets in complicated background is realized. The simulation results show that the feature extraction and recognition method has good robustness and high classification correct ratio, the combining recognition and tracking method also has high location precision.
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Acknowledgments
This work was jointly supported by the National Natural Science Foundation for Young Scientists of China (Grant No: 61202332, 61403397, 61503389), China Postdoctoral Science Foundation (Grant No: 2012M521905) and Natural Science Basic Research Plan in Shaanxi Province of China (Grant No: 2015JM6313).
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Wang, H., Cai, Y., Fu, G., Wu, M. (2017). Visual-Cognition-Driven SAR Multiple Targets Robust Feature Extraction, Recognition and Tracking. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_11
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DOI: https://doi.org/10.1007/978-981-10-5230-9_11
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