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Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set.

Jiulu Gong1, Guoliang Fan2, Liangjiang Yu3

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China. gongjiulu@gmail.com.

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Summary

We introduce ATR-Seg, a novel algorithm for infrared imagery that integrates target tracking, recognition, and segmentation. This method optimizes these tasks jointly, achieving accurate results without pre-processing.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Infrared imagery presents challenges for automated target recognition (ATR) due to variations in target appearance.
  • Existing ATR algorithms often require separate modules for tracking, recognition, and segmentation, leading to inefficiencies.

Purpose of the Study:

  • To develop an integrated algorithm for simultaneous target tracking, recognition, and segmentation in infrared imagery.
  • To improve the accuracy and efficiency of ATR systems by unifying these tasks within a single framework.

Main Methods:

  • A probabilistic shape-aware level set framework is employed, incorporating a joint view-identity manifold (JVIM) for target shape modeling.
  • JVIM utilizes a latent space with unified identity-independent and identity-dependent view manifolds.
  • The ATR problem is addressed as a sequential level-set optimization process over the JVIM latent space.

Main Results:

  • ATR-Seg achieves joint optimization of tracking and recognition through implicit shape matching.
  • Target segmentation is obtained as a byproduct, eliminating the need for pre-processing or feature extraction.
  • Experimental results on the SENSIAC ATR database show superior performance compared to existing ATR algorithms.

Conclusions:

  • ATR-Seg offers an effective and integrated solution for the ATR problem in infrared imagery.
  • The proposed JVIM framework enables robust target shape modeling and joint task optimization.
  • The algorithm demonstrates significant advantages over methods relying on explicit shape matching.