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<p>Small target recognition has always been challenging in image processing systems. When targets are far from the camera, target features tend to have low quality, limiting the amount of useful information for detection systems. Consequently, classic Detection Before Tracking (DBT) algorithms find great difficulty in separating targets from their background based on their visual properties. In this study, we proposed a Track Before Detect (TBD) approach that tracks potential targets in multiple frames, reducing the false alarm rate and enhancing the detection robustness to clutter. Then, we utilize target trajectory information to distinguish actual targets from any background noise. The proposed approach reframes the classic target image classification challenge to a multivariate time series classification problem, using target trajectory coordinates (x, y) as features. The proposed approach achieved a remarkable 97% accuracy in classifying targets from noise using only ten data points (half a second of tracking). Furthermore, it successfully classified targets into specific categories (airplane, drone, bird) with a 96% accuracy rate over a 1.5&#xa0;s window (30 data points).</p>

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Utilize trajectory information for small target classification

  • 期刊论文

  • 发表日期：

    2025年1月15日

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  • Saad Alkentar,
  • Abdulkarim Assalem,
  • Bassem Alsahwa

摘要

Small target recognition has always been challenging in image processing systems. When targets are far from the camera, target features tend to have low quality, limiting the amount of useful information for detection systems. Consequently, classic Detection Before Tracking (DBT) algorithms find great difficulty in separating targets from their background based on their visual properties. In this study, we proposed a Track Before Detect (TBD) approach that tracks potential targets in multiple frames, reducing the false alarm rate and enhancing the detection robustness to clutter. Then, we utilize target trajectory information to distinguish actual targets from any background noise. The proposed approach reframes the classic target image classification challenge to a multivariate time series classification problem, using target trajectory coordinates (x, y) as features. The proposed approach achieved a remarkable 97% accuracy in classifying targets from noise using only ten data points (half a second of tracking). Furthermore, it successfully classified targets into specific categories (airplane, drone, bird) with a 96% accuracy rate over a 1.5 s window (30 data points).