以非直觀之角度對用路人意向進行資料擷取

Data Acquisition of Pedestrian Intentions from Non-intuitive Angles

于建奕(Chien-I Yu)

指導教授 ： 劉寅春

淡江大學/工學院/電機工程學系碩士班/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00490

摘要

本研究以OpenPose作為人類行為意向估測之基礎，藉由拍攝行人行為意向改變時之影像取得其人體姿態之骨架與其中各關節之特徵點，建構人類行為意向之資料庫。
本研究提出利用特徵點所構成之角度當作判斷人類行為意向改變之依據。本研究利用低通濾波器剔除各特徵點中超出人類動作極限之高頻率擾動，從特徵點定義1092種不同角度，再透過均值濾波器消除雜訊干擾之浮動，防止系統做出誤判。
利用非監督式學習的K-means聚類分析，本研究針對行人行走之意向進行聚類分析。本研究透過側影函數以及肘點驗證法驗證不同聚類結果是否構成最佳分類群數，再藉由測試集檢測訓練後所構成之行為意向估測模型，確認模型準確率是否為其他聚類分析結果中最高。最終本研究得出針對行人行為意向之聚類分析模型，以此判定行人於影像中每幀行為意向為行走或停止兩種行為意向其一。
本研究發現影像資料需考慮不同情境下的變因，如夜間光線不足使其資料不合理。影像視角亦隨受測者位置變化。本研究以靜態拍攝路況之影像作為分析依據，現階段尚有需突破之限制，故未來會針對各類不同影像之資料處理進行突破以增加資料庫種類之全面性，以此優化分類結果取得更佳之判斷模型。

關鍵字

OpenPose ； K-Means聚類 ； 行為意向 ； 非直觀角度

並列摘要

This research uses OpenPose as the basis for human behavioral intention estimation. This research obtain the skeleton and keypoints of the pedestrian 's posture to constructs a database of pedestrian behavioral intentions.
This research proposes to use the angle formed by the keypoints as the basis for judging the change of human behavior intention.This study uses a low-pass filter to eliminate high-frequency disturbances that exceed the limits of human action in each keypoints,then defines 1092 different angles from the key points, and then eliminates the noise through the mean filter to prevent the system from making misjudgments.
Using the K-means cluster analysis of unsupervised learning, this study conducted a cluster analysis of pedestrians’ walking intentions. This study uses the silhouette and the elbow method to verify the best cluster, and then uses the test dataset to detect the behavioral intention estimation model after training to confirm whether the accuracy of the model is the highest the results. In the end, this study obtained a cluster analysis model for pedestrian behavior intentions, which can determine whether the pedestrian behavior intention in each frame of the video is one of two behavior intentions: walking or stopping.
This study found that the image data needs to consider the variable factors in different situations, such as insufficient light at night to make the data unreasonable. The viewing angle of the image also changes with the location of the subject. This research uses static images of road conditions as the basis for analysis. At this stage, there are still limitations that need to be overcome. Therefore, in the future, breakthroughs will be made in data processing of various different images to increase the comprehensiveness of the database types in order to optimize the classification results to obtain more Good judgment model.

並列關鍵字

OpenPose ； K-Means ； behavioral intention ； non-intuitive angle

參考文獻

[1] "Wikipedia self driving car," [Online]. Available: https://en.wikipedia.org/wiki/ Self-driving_car.

[2] Santokh Singh, "Critical reasons for crashes investigated in the national motor vehicle crash causation survey," Traffic Safety Facts - Crash Stats, pp. 1-2, 2015.

[3] Atsushi Tsukahara, Yasuhisa Hasegawa, Kiyoshi Eguchi and Yoshiyuki Sankai, "Restoration of Gait for Spinal Cord Injury Patients Using HAL With Intention Estimator for Preferable Swing Speed," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 2, pp. 308-318, 2015.

[4] Jian Huang, Weiguang Huo, Wenxia Xu, Samer Mohammed and Yacine Amirat, "Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition," IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1257-1270, 2015.

[5] Lichao Wang, Karim Lekadir, Su-Lin Lee, Robert Merrifield and Guang-Zhong Yang, "A general framework for context-specific image segmentation using reinforcement learning," IEEE Trans actions on Medical Imaging, vol. 32, no. 5, pp. 943-956, 2013.