[논문]HigherHRNet 기반의 발추정 기법을 통한 횡단보도 보행자 인식

정경민; 한주훈; 이현

doi:10.14372/iemek.2021.16.5.171

HigherHRNet 기반의 발추정 기법을 통한 횡단보도 보행자 인식
Pedestrian Recognition of Crosswalks Using Foot Estimation Techniques Based on HigherHRNet 원문보기

대한임베디드공학회논문지 = IEMEK Journal of embedded systems and applications, v.16 no.5, 2021년, pp.171 - 177

정경민 (Sunmoon University) , 한주훈 (Thinkwintek co.) , 이현 (Sunmoon University)

Abstract ▼ AI-Helper

It is difficult to accurately extract features of pedestrian because the pedestrian is photographed at a crosswalk using a camera positioned higher than the pedestrian. In addition, it is more difficult to extract features when a part of the pedestrian's body is covered by an umbrella or parasol or when the pedestrian is holding an object. Representative methods to solve this problem include Object Detection, Instance Segmentation, and Pose Estimation. Among them, this study intends to use the Pose Estimation method. In particular, we intend to increase the recognition rate of pedestrians in crosswalks by maintaining the image resolution through HigherHRNet and applying the foot estimation technique. Finally, we show the superiority of the proposed method by applying and analyzing several data sets covered by body parts to the existing method and the proposed method.

주제어

표/그림 (11)

그림 그림 1. 자세추정 방법 중 (a) 하향식, (b) 상향식 방법의 예시 Fig. 1. Example of (a) top-down, (b) bottom-up methods of pose estimation
그림 그림 2. HRNet의 전체 구조 Fig. 2. Overall structure of HRNet
그림 그림 3. (a) HRNet (b) HigherHRNet의 차이점 Fig. 3. Differences in (a) HRNet, (b) HigherHRNet
그림 그림 4. Foot + HigherHRNet의 전체 구조 Fig. 4. Overall structure of Foot + HigherHRNet
그림 그림 5. Human Foot Keypoint Dataset을 활용한 실험 데이터 예시 Fig. 5. Example of experimental data using Human Foot Keypoint Dataset
표 표 1. 발 추정 기반 보행자 인식 모델의 parameter 설정 값 Table 1. Parameter setting value of foot estimation-based pedestrian recognition model
그림 그림 6. 실험데이터에 대한 (a) HigherHRNet (b) Foot + HigherHRNet 검출 차이 Fig. 6. Difference in detection of (a) Higher HRNet (b) Foot + Higher HRNet for experimental data
그림 그림 7. (a) HigherHRNet, (b) Foot + HigherHRNet의 가려져 있는 정도에 따른 검출 차이 Fig. 7. Differences in detection depending on the degree of occlusion of (a)HigherHRNet, (b)Foot + HigherHRNet
그림 그림 8. (a) HigherHRNet (b) Foot + HigherHRNet의 75% 가려져있을 때 검출 차이 Fig. 8. Detection difference when pedestrians are 75% hidden (a) HigherHRNet, (b)Foot + HigherHRNet
그림 그림 9. (a) HigherHRNet (b) Foot + HigherHRNet의 양산, 우산으로 가려져있는 횡단 보도 보행자에 대한 검출 결과 Fig. 8. (a) HigherHRNet, (b)Foot + HigherHRNet Detection results of pedestrians on pedestrian crossings wrapped in umbrellas and parasols
표 표 2. (a) Foot + HigherHRNet, (b) HigherHRNet를 통한 검증용 데이터의 Average Precision (AP), Average Recall (AR) 결과 Table 2. Average Precision (AP), Average Recall (AR) results for validation data through (a) Foot + HigherHRNet, (b) HigherHRNet.

참고문헌 (24)

S. Ren, K. He, R. Girshick, J. Sun, "Faster R-cnn: Towards Real-time Object Detection with Region Proposal Networks," Advances in Neural Information Processing Systems, 28, 91-99. 2015.
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, "You Only Look Once: Unified, Real-time Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 779-788. 2016.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, A.C. Berg, "Ssd: Single Shot Multibox DDetector," In European Conference on Computer Vision., Springer, Cham, pp. 21-37. 2016.
J. Dai, Y. Li, K. He, J. Sun, "R-fcn: Object Detection via Region-based Fully Convolutional Networks," In Advances in Neural Information Processing Systems pp. 379-387, 2016.
J. Redmon, A. Farhadi, "YOLO9000: Better, Faster, Stronger," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271, 2017.
T.Y. Lin, P. Dollar, R. Girshick, K. He B. Hariharan, S. Belongie, "Feature Pyramid Networks for Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125. 2017.
T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, "Focal Loss for Dense Object Detection," In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, 2017.
K. He, G. Gkioxari, P. Dollar, R. Girshick, "Mask r-cnn," In Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969, 2017.
J. Redmon, A. Farhadi, "Yolov3: An Incremental Improvement," arXiv preprint, arXiv:1804.02767, 2018.
S. Zhang, L. Wen, X. Bian, Z. Lei, S.Z. Li, "Single-shot Refinement Neural Network for Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203-4212, 2018.
Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, H. Ling, "M2det: A Single-shot Object Detector Based on Multi-level Feature Pyramid Network," In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 9259-9266, 2019.
S. Liu, L. Qi, H. Qin, J. Shi, J. Jia, "Path aggregation network for instance segmentation," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.8759-8768, 2018.
D. Bolya, C. Zhou, F. Xiao, Y.J. Lee, "Yolact: Real-time Instance Segmentation," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9157-9166, 2019.
B. Cheng, B. Xiao, J. Wang, H. Shi, T.S. Huang, L. Zhang, "Higherhrnet: Scale-aware Representation Learning for Bottom-up Human Pose Estimation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5386-5395, 2020.
S. Jin, L. Xu, J. Xu, C. Wang, W. Liu, C. Qian, W. Ouyang, P. Luo, "Whole-body Human Pose Estimation in the Wild," In European Conference on Computer Vision, Springer, Cham, pp. 196-21, 2020.
Z. Cao, G. Hidalgo, T. Simon, S.E. Wei, Y. Sheikh, "OpenPose: Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields," IEEE transactions on pattern analysis and machine intelligence, 43(1), 172-186, 2019.
J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, B. Xiao, "Deep High-resolution Representation Learning for Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P.V. Gehler, B. Schiele, "Deepcut: Joint Subset Partition and Labeling for Multi Person Pose Estimation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4929-4937, 2016.
Z. Cao, T. Simon, S.E. Wei, Y. Sheikh, "Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299, 2017.
G. Papandreou, T. Zhu, L.C. Chen, S. Gidaris, J. Tompson, K. Murphy, "Personlab: Person Pose Estimation and Instance Segmentation with a Bottom-up, Part-based, Geometric Embedding Model," In Proceedings of the European Conference on Computer Vision (ECCV), pp. 269-286, 2018.
R. Girshick, "Fast r-cnn," In Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C.L. Zitnick, "Microsoft coco: Common Objects in Context," In European Conference on Computer Vision, pp. 740-755, 2014.
A. Newell, Z. Huang, J. Deng, "Associative Embedding: End-to-end Learning for Joint Detection and Grouping," arXiv preprint, arXiv:1611.05424, 2016.
https://github.com/open-mmlab/mmpose

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