[1] [1] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6): 1137-1149.

[3] [3] Duan K W, Bai O, Xie L X, et al. CenterNet: Keypoint triplets for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 6568-6577.

[4] [4] Zhou X Y, Zhou J C, Krahenbuhl P. Bottom-up object detection by grouping extreme and center points[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 850-859.

[9] [9] Liu W, Liao S C, Ren W Q, et al. High-level semantic feature detection: A new perspective for pedestrian detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 5182-5191.

[10] [10] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//IEEE International Conference on Computer Vision, 2017: 2999-3007.

[11] [11] Sun X, Xiao B, Wei F Y, et al. Integral human pose regression[C]//European Conference on Computer Vision, 2018: 529-545.

[12] [12] Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936-944.

[13] [13] Li D, Yao A B, Chen Q F. PSConv: Squeezing feature pyramid into one compact poly scale convolutional layer[C]//European Conference on Computer Vision, 2020: 615-632.

[14] [14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.

[15] [15] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module[C]//European Conference on Computer Vision, 2018: 3-19.

[16] [16] Zagoruyko S, Komodakis N. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer[C]//International Conference on Learning Representations, 2017: 128-139.

[17] [17] Zhang S, Benenson R, Schiele B. CityPersons: A diverse dataset for pedestrian detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4457-4465.

[18] [18] Zheng Z H, Wang P, Liu W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]//AAAI Conference on Artificial Intelligence, 2020.

[19] [19] Lin C Y, Xie H X, Zheng H. PedJointNet: Joint head-shoulder and full body deep network for pedestrian detection[J]. IEEE Access, 2019, 7: 47687-47697.

[20] [20] Song T, Sun L Y, Xie D, et al. Small-scale pedestrian detection based on topological line localization and temporal feature aggregation[C]//European Conference on Computer Vision, 2018: 536-551.

[21] [21] Li J N, Liang X D, Shen S M, et al. Scale-aware fast R-CNN for pedestrian detection[J]. IEEE Transactions on Multimedia, 2017, 20(4): 985-996.

[22] [22] Mao J Y, Xiao T, Jiang Y N, et al. What can help pedestrian detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3127-3136.

[23] [23] Ma J, Wan H L, Wang J X, et al. An improved scheme of deep dilated feature extraction on pedestrian detection[J]. Signal, Image and Video Processing, 2021, 15(2): 231-239.