• Infrared and Laser Engineering
  • Vol. 50, Issue 11, 20210206 (2021)
Lu Zhao1 and Sen Xiong2
Author Affiliations
  • 1Department of Engineering, Xi'an Siyuan University, Xi'an 710038, China
  • 2Xi'an Sitan Instrument Co., Ltd., Xi'an 710065, China
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    DOI: 10.3788/IRLA20210206 Cite this Article
    Lu Zhao, Sen Xiong. Target recognition based on multi-view infrared images[J]. Infrared and Laser Engineering, 2021, 50(11): 20210206 Copy Citation Text show less
    Flowchart of infrared image target recognition based on multi-view
    Fig. 1. Flowchart of infrared image target recognition based on multi-view
    Illustration of infrared images of the four vehicles
    Fig. 2. Illustration of infrared images of the four vehicles
    Performance comparison of different methods on noisy samples
    Fig. 3. Performance comparison of different methods on noisy samples
    Performance comparison of different methods on occluded samples
    Fig. 4. Performance comparison of different methods on occluded samples
    ${I_1}$${I_2}$$ \cdots $${I_N}$
    ${I_1}$${c_{11}}$${c_{12}}$$ \cdots $${c_{1N}}$
    ${I_2}$${c_{21}}$${c_{22}}$$ \cdots $${c_{2N}}$
    $ \vdots $$ \vdots $$ \vdots $$ \ddots $$ \vdots $
    ${I_N}$${c_{N1}}$${c_{N2}}$$ \cdots $${c_{NN}}$
    Table 1. Similarity matrix of multi-view infrared images
    ClassBusCarTruckVanRecognition rate
    Bus1576561398.50%
    Car9267471099.04%
    Truck41592398.67%
    Van23688998.78%
    Average recognition rate98.81%
    Table 2. Recognition results of the proposed method on the original samples of four targets
    MethodsAverage recognition rate
    Proposed98.81%
    SRC97.68%
    SVM97.04%
    HOG97.23%
    Deep features98.02%
    Table 3. Performance comparison of different methods on orginal samples
    Lu Zhao, Sen Xiong. Target recognition based on multi-view infrared images[J]. Infrared and Laser Engineering, 2021, 50(11): 20210206
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