基於OpenPose特徵的行人分心偵測

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姓名

廖偉翔(WEI-XIANG LIAO) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於OpenPose特徵的行人分心偵測
(Detecting distracted pedestrian using OpenPose features)

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摘要(中)

根據研究顯示，不管是駕駛者或行人，其分心的行為都將對安全造成嚴重影響，因此若能避免分心的行為，將能使交通意外發生的機率降低。然而過去研究大多針對駕駛者的分心行為進行偵測，較少研究針對行人的分心行為進行偵測，因此，本研究的目的為透過自動偵測行人分心行為的技術，來改善駕駛者與行人的安全問題，當能夠精準的偵測分心的行人之後，可以透過干預措施來改善交通安全，譬如發送訊息或信號給駕駛者和行人，除此之外，此技術也能使用在自動駕駛或是先進駕駛輔助系統(ADAS)，當偵測到行人分心時，可以透過減速或其他的預防措施來避免意外的發生。
本論文基於OpenPose特徵進行行人分心偵測，論文中提出新的CNN架構，使用OpenPose的中間層特徵圖當作CNN的輸入 (OpenPose-based CNN)，取代以影像做為CNN的輸入 (Image-based CNN)，使EER改善33.33% (EER = 8%)，除此之外，實驗發現Skeleton-based SVM與OpenPose-based CNN可以處理不同的資料，因此將兩個模型進行Ensemble，相比只使用單一模型可使EER改善20% (EER = 6.4%)。最後我們嘗試使用連續的多張影像進行識別，相比以單張影像進行識別可使EER改善42.19% (EER = 3.7%)。

摘要(英)

According to previous researches, both driver and pedestrian distraction will have a serious impact on safety. Therefore, if the distracting behavior can be avoided, the probability of traffic accidents will be reduced. However, in the past, most researches focused on the detection of distracted driver. There are limited researches which focused to detect distraction among pedestrians. Therefore, the goal of this research is to improve both the driver and pedestrian safety by automatically detect distracted pedestrian. After accurately detecting the distracted pedestrian, interventions can be applied to improve traffic safety, such as sending warning messages or signals to drivers and pedestrians. In addition, this technology can also be used in self-driving car or advanced driver assistance system (ADAS). ADAS can help avoid accident through deceleration or other preventive methods after distracted pedestrian is detected.
In this paper, we propose a new distracted pedestrian detection method based on the OpenPose features, and propose a new CNN architecture, using OpenPose′s intermediate layer feature map as CNN input (OpenPose-based CNN), compared with using images as CNN input (image-based CNN), EER can be improved by 33.33% (EER = 8%). In addition, the experiment found that Skeleton-based SVM and OpenPose-based CNN can handle different type of data, so ensemble the two models can improve EER by 20% (EER = 6.4%). Finally, we try to use multiple continuously images for recognition, which can improve EER by 42.19% (EER = 3.7%) compared to use single image for recognition.

關鍵字(中)

★ 行人分心偵測
★ 計算機視覺
★ SVM
★ CNN
★ OpenPose

關鍵字(英)

★ Distracted pedestrian detection
★ Computer vision
★ SVM
★ CNN
★ OpenPose

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 3
1-4 研究貢獻 4
1-5 論文架構 5
二、相關研究 6
2-1 效能評估指標 6
2-2 行人分心相關研究 7
2-3 OpenPose 8
三、系統架構 11
3-1 系統輸入 12
3-2 行人偵測 12
3-3 行人追蹤 17
3-4 影像前處理 18
3-5 2D姿態估計 19
3-6 行為識別 21
四、分心識別方法 22
4-1 識別方法介紹 22
4-2 識別模型 25
4-2-1 Skeleton-based SVM 26
4-2-2 Image-based CNN 28
4-2-3 OpenPose-based CNN 29
4-2-4 3D-CNN 34
4-3 模型集成 35
五、實驗與討論 37
5-1 資料集 37
5-1-1 資料集介紹 37
5-1-2 資料切割方法 39
5-2 CNN訓練方法 40
5-3 實驗一 : 使用CNN解決Skeleton-based SVM誤判及無法處理之影像 42
5-3-1 實驗動機 42
5-3-2 實驗目的 42
5-3-3 實驗方法 42
5-3-4 實驗結果 44
5-4 實驗二 : 使用OpenPose-based CNN取代Image-based CNN 47
5-4-1 實驗動機 47
5-4-2 實驗目的 47
5-4-3 實驗方法 47
5-4-4 實驗結果 49
5-5 實驗三 : 使用Stacking的方法，取代Voting的方法 49
5-5-1 實驗動機 49
5-5-2 實驗目的 50
5-5-3 實驗方法 50
5-5-4 實驗結果 52
5-6 實驗四 : 使用多張影像進行時序型的識別方法取代以單張影像進行非時序的識別方法 54
5-6-1實驗動機 54
5-6-2 實驗目的 54
5-6-3 實驗方法 55
5-6-4 實驗結果 56
六、結論與未來研究 60
6-1 論文總結 60
6-2 未來研究 60
參考文獻 62

參考文獻

[1] W. H. Organization, Global status report on road safety 2018. World Health Organization, 2018.
[2] F. Guo et al., "The effects of age on crash risk associated with driver distraction," International journal of epidemiology, vol. 46, no. 1, pp. 258-265, 2017.
[3] Z. Zhou, S. Liu, W. Xu, Z. Pu, S. Zhang, and Y. Zhou, "Impacts of mobile phone distractions on pedestrian crossing behavior at signalized intersections: An observational study in China," Advances in Mechanical Engineering, vol. 11, no. 4, p. 1687814019841838, 2019.
[4] H. Zhang, C. Zhang, F. Chen, and Y. Wei, "Effects of mobile phone use on pedestrian crossing behavior and safety at unsignalized intersections," Canadian Journal of Civil Engineering, vol. 46, no. 5, pp. 381-388, 2019.
[5] W. Yuanyuan, Z. Cunbao, Z. Bin, C. Feng, and Z. Hualong, "The mobile phone use behavior and its effect on pedestrian safety at signalized intersections in China," in 2017 4th International Conference on Transportation Information and Safety (ICTIS), 2017: IEEE, pp. 225-231.
[6] B. Le, C. Figueroa, C. Anderson, S. Lotfipour, and C. Barrios, "Determining the incidence of distraction among trauma patients in all modes of transportation," Journal of trauma and acute care surgery, vol. 87, no. 1, pp. 87-91, 2019.
[7] J. L. Nasar and D. Troyer, "Pedestrian injuries due to mobile phone use in public places," Accident Analysis & Prevention, vol. 57, pp. 91-95, 2013.
[8] D. L. Strayer and F. A. Drew, "Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers," Human factors, vol. 46, no. 4, pp. 640-649, 2004.
[9] G. Selamaj, "Impacts of Mobile Phone Distractions on Walking Performance," Indonesian Journal of Computing, Engineering and Design (IJoCED), vol. 2, no. 1, pp. 32-37, 2020.
[10] T. Hoang Ngan Le, Y. Zheng, C. Zhu, K. Luu, and M. Savvides, "Multiple scale faster-rcnn approach to driver′s cell-phone usage and hands on steering wheel detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016, pp. 46-53.
[11] W. Kim, H.-K. Choi, B.-T. Jang, and J. Lim, "Driver distraction detection using single convolutional neural network," in 2017 international conference on information and communication technology convergence (ICTC), 2017: IEEE, pp. 1203-1205.
[12] H. M. Eraqi, Y. Abouelnaga, M. H. Saad, and M. N. Moustafa, "Driver distraction identification with an ensemble of convolutional neural networks," Journal of Advanced Transportation, vol. 2019, 2019.
[13] A. Rangesh, E. Ohn-Bar, K. Yuen, and M. M. Trivedi, "Pedestrians and their phones-detecting phone-based activities of pedestrians for autonomous vehicles," in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016: IEEE, pp. 1882-1887.
[14] A. Rangesh and M. M. Trivedi, "When Vehicles See Pedestrians With Phones: A Multicue Framework for Recognizing Phone-Based Activities of Pedestrians," IEEE Transactions on Intelligent Vehicles, vol. 3, no. 2, pp. 218-227, 2018.
[15] K. Kumamoto and K. Yamada, "Detecting Interaction of Pedestrians with Their Smartphones Based on Body Keypoints," in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018: IEEE, pp. 3261-3266.
[16] J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
[17] G.-S. Xia et al., "DOTA: A large-scale dataset for object detection in aerial images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 3974-3983.
[18] T.-Y. Lin et al., "Microsoft coco: Common objects in context," in European conference on computer vision, 2014: Springer, pp. 740-755.
[19] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple online and realtime tracking," in 2016 IEEE International Conference on Image Processing (ICIP), 2016: IEEE, pp. 3464-3468.
[20] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7291-7299.
[21] Z. Fang, D. Vázquez, and A. M. López, "On-board detection of pedestrian intentions," Sensors, vol. 17, no. 10, p. 2193, 2017.
[22] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[23] S. Ji, W. Xu, M. Yang, and K. Yu, "3D convolutional neural networks for human action recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 1, pp. 221-231, 2012.
[24] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626.

指導教授

梁德容張欽圳(Deron Liang Chin-Chun Chang)

審核日期

2020-7-24

推文