Figures (6)  Tables (2)

    • Figure 1. 

      Overall framework of the study.

    • Figure 2. 

      HAERC model framework.

    • Figure 3. 

      Comparison of classification processes between NMS and ENMS.

    • Figure 4. 

      Distribution of occlusion ratio in the pedestrian dataset.

    • Figure 5. 

      FPPI Miss rate image.

    • Figure 6. 

      Detection performance of different models.

    • Heavy Reasonable
      Accuracy Recall F1 score FPPI Accuracy Recall F1 score FPPI
      ALFNet 57.34% 63.70% 65.49% 51.90% 70.81% 64.12% 70.87% 12%
      Rep Loss 55.26% 62.31% 55.00% 64.12% 68.24% 62.04% 68.71% 13.20%
      Faster R-CNN 56.36% 54.00% 65.51% 55.67% 66.45% 62.35% 67.97% 14.37%
      YOLOv8 57.70% 63.53% 66.59% 44.24% 72.05% 61.12% 68.59% 12.07%
      HAERC 60.68% 63.70% 68.09% 46.64% 73.33% 62.40% 69.83% 9.59%
      Partial Bare
      Accuracy Recall F1 score FPPI Accuracy Recall F1 score FPPI
      ALFNet 74.39% 60.94% 69.73% 11.40% 74.82% 60.94% 69.86% 8.40%
      Rep Loss 69.99% 59.88% 67.34% 16.80% 75.51% 60.28% 69.56% 7.60%
      Faster R-CNN 70.74% 59.47% 67.30% 15.84% 74.92% 60.56% 69.39% 8.13%
      YOLOv8 75.10% 61.32% 70.25% 9.86% 76.54% 60.83% 70.31% 7.45%
      HAERC 75.91% 61.05% 70.30% 9.43% 77.16% 60.51% 70.24% 6.78%
      The bold part indicates that the optimality can be strengthened.

      Table 1. 

      Performance comparison with other occlusion models.

    • Model ENMS Head feature
      module      		 Backbone Heavy Reasonable
      HAERC1 √ ResNet-50 49.74 11.67
      HAERC2 √ ResNet-50 47.3 10.74
      HAERC √ √ ResNet-50 46.64 9.59

      Table 2. 

      Results of ablation experiments.