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Volume 44 Issue 11
Nov.  2022
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WU Jie, MA Xiaohu. Anti-Occlusion Infrared Target Tracking AlgorithmBased on Fusion of Discriminant and Fine-Grained Features[J]. Infrared Technology , 2022, 44(11): 1139-1145.
Citation: WU Jie, MA Xiaohu. Anti-Occlusion Infrared Target Tracking AlgorithmBased on Fusion of Discriminant and Fine-Grained Features[J]. Infrared Technology , 2022, 44(11): 1139-1145.
shu

Anti-Occlusion Infrared Target Tracking AlgorithmBased on Fusion of Discriminant and Fine-Grained Features

  • 1.

    College of Information technology, Taizhou Polytechnic College, Taizhou 225300, China

  • 2.

    School of Computer Science and Technology, Soochow University, Suzhou 215006, China

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  • Received Date: June 17, 2022
  • Revised Date: August 30, 2022
    Graphical Abstract
    • Abstract
  • Considering the problem in which the existing thermal infrared target tracking algorithms have difficulty dealing with similar object interference and target occlusion, the multi-task framework in the MMNet algorithm is introduced to obtain the specific discriminant features and fine-grained features of thermal infrared targets, which are fused to identify thermal infrared objects between and within classes. In addition, the peak side-lobe ratio is adopted to dynamically set the model update parameters and obtain the target change information more efficiently, in addition to evaluating the tracking results. For unreliable tracking results, a Kalman filter was unutilized to predict the target. The experimental results on the LSOTB-TIR dataset demonstrated that the performance of the improved algorithm was optimal. Compared with MMNet, the tracking accuracy and success rate were improved by 5.7% and 4.2%, respectively. It can effectively address the challenges of occlusion and deformation and can also be applied to the field of infrared target tracking.
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    • References (16)
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