PedCross: Pedestrian Crossing Prediction for Auto-driving Bus

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邱翊倫(Yi-Lun Chiu) 查詢紙本館藏

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資訊工程學系

論文名稱

(PedCross: Pedestrian Crossing Prediction for Auto-driving Bus)

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摘要(中)

自動駕駛是近來熱門的研究題目。目前已經有許多關於物體檢測和追蹤研究工作，而研究重點預計會轉移到物體運動的預測上。在不同類型的物體中，行人是更難預測的，因為人類可以快速地改變方向和狀態（例如，行走或停止）。在這項研究中，我們建立了一個名為 PedCross 的系統，使用人類的圖像語義信息來預測行人的行為（即穿越馬路或不穿越馬路）。在這個系統中，首先會使用行人的影像來檢測人體骨骼。然後藉由人體骨骼來提取特徵值，並用於模型的訓練。Random Forest和 LSTM這兩種類型的模型，會被用於人行穿越馬路的預測。為了進一步提高整個系統的效率和正確率，我們在系統中加入了Skip Frame、Head Orientation 和 Warning/Dangerous Zones這些元件。 PedCross 不僅使用真實場景中收集到的 ITRI dataset進行測試，還實際部署在自動駕駛巴士上，並且進行道路測試。道路測試的結果顯示，PedCross 達到了工研院提出的所有要求，並且優於工研院開發的基準系統 Free Space。

摘要(英)

The topic of autonomous driving has become a popular research subject recently. Many research works have been on object detection and tracking, but it is expected that the focus will eventually be shifted to the prediction of object movements. Among different types of objects, pedestrian movements are more difficult to predict because humans can change their direction and status (e.g., walking or stopping) quickly. In this research, we build a system, called PedCross, which uses human image semantic information to predicts the behavior of pedestrians (i.e., crossing or not crossing). In PedCross, images of pedestrians are first used to detect skeletons. The features in the detected skeletons are then extracted for model training. Two types of models, Random Forest and LSTM, are considered for pedestrian crossing prediction. To further improve the efficiency and accuracy of PedCross, Skip Frame, Head Orientation, and Warning/Dangerous Zones are integrated. PedCross is not only tested with the collected ITRI dataset but also deployed on auto-driving bus for road test. The road test indicates that PedCross achieves all the requirements set forth by ITRI and outperforms Free Space, a baseline system developed by ITRI.

關鍵字(中)

★ 行人
★ 自動駕駛
★ 機器學習
★ 深度學習
★ 影像資訊

關鍵字(英)

★ Pedestrian
★ Auto-driving
★ Machine learning
★ Deep learning
★ Image information

論文目次

1 Introduction 1
2 Related Work 4
2.1 Pedestrian Path Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . .4
2.2 Pedestrian Intention Prediction . . . . . . . . . . . . . . . . . . . . . . . .5
3 Preliminary 6
3.1 Objects Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
3.1.1 YOLOv3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
3.2 Objects Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
3.2.1 SORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
3.3 Skeleton Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
3.3.1 OpenPose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
3.4 Supervised Learning Method . . . . . . . . . . . . . . . . . . . . . . . . . .8
3.4.1 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
3.4.2 LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
3.5 Robot Operating System . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
3.6 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
3.7 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Design 11
4.1 Skeleton Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.1 OpenPose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.2 Skip Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.4 Head Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.5 Warning Zone and Dangerous Zone . . . . . . . . . . . . . . . . . . . . . . 21
5 Performance 23
5.1 Environmental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.2 Considered Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.4 Dataset Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.5 Model Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.6 Performance Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . 32
6 Conclusions 36
Reference 37

參考文獻

[1] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. Social lstm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June 2016.
[2] 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), pages3464–3468, 2016.
[3] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection, 2020.
[4] Z. Cao, G. Hidalgo Martinez, T. Simon, S. Wei, and Y. A. Sheikh. Openpose: Realtime multi-person 2d pose estimation using part affinity fields.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
[5] Cuda. https://developer.nvidia.com/cuda-zone.
[6] Z. Fang and A. M. L ́opez. Is the pedestrian going to cross? answering by 2d pose estimation. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 1271–1276,2018.
[7] T. Gandhi and M. M. Trivedi. Image based estimation of pedestrian orientation for improving path prediction. In 2008 IEEE Intelligent Vehicles Symposium, pages 506–511, 2008.
[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015.
[9] Sepp Hochreiter and J ̈urgen Schmidhuber. Long short-term memory.Neural Computation, 9(8):1735–1780, 1997.
[10] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35–45, 03 1960.[11] H. W. Kuhn. The hungarian method for the assignment problem.Naval Research Logistic Quarterly, 2:83–97, 1955.
[12] J. Liang, L. Jiang, J. C. Niebles, A. G. Hauptmann, and L. Fei-Fei. Peeking into the future: Predicting future person activities and locations in videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5718–5727, 2019.
[13] B. Liu, E. Adeli, Z. Cao, K. Lee, A. Shenoi, A. Gaidon, and J. C. Niebles. Spatiotemporal relationship reasoning for pedestrian intent prediction. IEEE Roboticsand Automation Letters, 5(2):3485–3492, 2020.
[14] Y. L. Murphey, C. Liu, M. Tayyab, and D. Narayan. Accurate pedestrian path prediction using neural networks. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7, 2017.
[15] nvidia-smi. https://developer.nvidia.com/nvidia-system-management-interface.
[16] Opencv. https://opencv.org.
[17] Atanas Poibrenski, Matthias Klusch, Igor Vozniak, and Christian M ̈uller. Multimodal multi-pedestrian path prediction for autonomous cars. SIGAPP Appl. Comput. Rev.,20(4):5–17, January 2021.
[18] Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, Andrew Y Ng, et al. Ros: an open-source robot operating system. In ICRA workshop on open source software, volume 3, page 5. Kobe, Japan, 2009.
[19] A. Rasouli, I. Kotseruba, T. Kunic, and J. Tsotsos. Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6261–6270, 2019.
[20] A. Rasouli, I. Kotseruba, and J. K. Tsotsos. Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pages 206–213, 2017.
[21] Joseph Redmon and Ali Farhadi. Yolo9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242, 2016.
[22] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. CoRR, abs/1804.02767, 2018.
[23] E. Rehder, F. Wirth, M. Lauer, and C. Stiller. Pedestrian prediction by planning using deep neural networks. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 5903–5908, 2018.
[24] David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533–536, Oct 1986.
[25] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition, 2015.
[26] Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional generative models. Advances in neural information processing systems, 28:3483–3491, 2015.
[27] Stephen V Stehman. Selecting and interpreting measures of thematic classification accuracy. Remote sensing of Environment, 62(1):77–89, 1997.
[28] Tensorflow. https://www.tensorflow.org/.
[29] Tensorrt. https://github.com/NVIDIA/TensorRT.
[30] Tin Kam Ho. Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, volume 1, pages 278–282 vol.1, 1995.
[31] Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric, 2017.

指導教授

孫敏德(Min-Te Sun)

審核日期

2021-8-3

推文