Intelligent Robot Lab
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    電腦視覺組致力於智慧型運輸系統的開發,主要的研究方向為駕駛警示的輔助系統,
    包含行人偵測、車輛偵測等主題,營造未來友善的交通環境。
    The Computer Vision Group dedicates to the development of Intelligent Transportation System.
    The assistive alarming system to drive is our major research area, including pedestrian detection and vehicle detection.
    虛擬實境組的研究專注於個人化的動作辨識,將辨識的技術使用於人機互動、擴增實境等應用,
    能在未來以虛擬實境輔助人類日常活動及提升生活的便利性。
    The Virtual Reality Group focus on the personalized action recognition
    which is applied in Human-Machine Interaction and Augmented Reality.
    The technique is expected to assist human’s daily activity as well as to enhance the convenience of future life.













智慧型運輸系統

    在智慧型型運輸系統中,進階車輛控制與安全系統(AVCSS)是一個重要的議題:經由一台工業用電腦,應用攝影機或與其他感應技術的結合獲得車輛前方道路場景的資訊,並分析或指出是否存在危險的情況。這種系統提供一個警示的裝置避免駕駛因疲勞或短暫的不注意而發生車禍。經由上述的理由,我們發展出一連串以電腦視覺(computer vision)為基礎的技術,包含了道路線辨識和障礙物偵測(行人或車輛偵測)。

Intelligent Transportation System (ITS)

    Advanced Vehicle Control and Safety System (AVCSS) is an important topic in Intelligent Transportation Systems (ITS), which applies the cameras or other sensor fusion techniques to obtain information from road scene in front of vehicle, and prevents from the dangerous situations. The systems in the topic provide a warning mechanism to protect drivers from car accidents caused by fatigue or sudden distraction. According to this reason, we have developed a series of techniques based on computer vision techniques including lane recognition and obstacle detection (such as pedestrian or vehicle detection).

行人偵測

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    行人偵測能運用在許多不同系統上,譬如人機互動系統、安全監控系統與型車駕駛輔助等方面,主要遭遇的問題為複雜的行人姿態與光影變化。在多變的環境中要達到良好的偵測率,針對完善且複雜的資料庫,我們提出一個系統化的機器學習流程,目標在於克服大型資料庫中內類別變異性太大的問題。在影像中找出行人特有的特徵並利用學習演算法將行人從影像中定位出來。

Pedestrian Detection

    The motivation of system is that many pedestrians were injured due to collisions with vehicles. Here we have developed pedestrian detection systems to avoid these accidents by reminding the drivers to pay attention to the surrounding situation. This system is a hierarchical template-based matching, so it is fast and efficient. And it has been widely applied to Intelligence Transportation Systems or some applications of surveillance.

行人與車輛辨識追蹤之感測融合技術

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    研究設計一套可安裝於車輛之標的障礙物(行人、類行人與汽車)分類器辨識系統,可在系統啟動時,隨即建構出視覺化的駕駛環境人機介面,並顯示於螢幕上,當偵測到標的障礙物時,輸出障礙物之相對於車輛位置並顯示其影像座標資訊,根據標的障礙物座標資訊提供即時警示,以有效預防意外事故之發生。

Using Fusion Techniques To Detect Pedestrians And Vehicles

    The topic aims to design a vehicle-equipped detection system which is able to set up a visualized Human-Machine Interface (HCI) of driving environments. At the moment of detecting target obstacles, it provides the warnings according to the relative location between obstacles and vehicles in order to efficiently prevent from car accidents.

個人化的動作辨識

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    個人化動作辨識研究之目的在於想要建立一個可靠的通用型動作辨識系統,其中之困難處在於適應不同使用者有不同的動作習慣與外觀,針對這些難題尋求解決之道,則將可以迅速有效地建立個人化的動作辨識系統,進而運用在即時的互動應用,能以動作為輸入訊號融合於仰賴固定型態動作辨識的系統,提升未來日常生活上的便利性以及在虛擬實境的延伸技術上有多方面的發展。

User-Specific Motion Recognition

    This research focuses on developing a reliable and general action-recognition (AR) system, which is difficult to adapt various appearance and action styles of different users. Many future applications rely on this technique such as the real-time Human-Machine Interaction (HCI) systems due to the significance of identifying the input signal of a certain motion pattern, and are potentially used in extension techniques of AR as well as enhance the convenience of life.

Title Name Duration Email

碩士研究生    M.S. student

沈宗穎 Zong-Ying Shen (2015-) colin8930 at gmail.com

碩士研究生    M.S. student

吳忞諭 Min-Yu Wu (2015-) kyrtse at hotmail.com

碩士研究生    M.S. student

塗國星 Kuo-Hsin Tu (2015-) p04922004 at ntu.edu.tw

碩士研究生    M.S. student

丁柏文 Po-Wen Ting (2016-) ck980046 at gmail.com

碩士研究生    M.S. student

葉興宇 Hsing-Yu Yeh (2016-) yehhsingyu1029 at qq.com

碩士研究生    M.S. student

韓翔宇 Hsiang-Yu Han (2016-) andy8517251 at gmail.com
Online view-invariant human action recognition using rgb-d spatiotemporal matrix Yen-Pin Hsu, Chengyin Liu, Tzu-Yang Chen, Li-Chen Fu Pattern Recognition (PR), vol. 60, pp.215-226, Dec 2016
Pang-Ting Huang; Yi-Ming Chan; Li-Chen Fu; Shih-Shinh Huang; Pei-Yung Hsiao; Wei-Yu Wu; Chun-Cheng Lin; Kuo-Ching Chang; Ping-Min Hsu, "Pedestrian detection system in low illumination conditions through Fusion of image and range data," Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on , vol., no., pp.2253,2254, 8-11 Oct. 2014
Han-Hsuan Chen; Chun-Cheng Lin; Wei-Yu Wu; Yi-Ming Chan; Li-Chen Fu; Pei-Yung Hsiao, "Integrating appearance and edge features for on-road bicycle and motorcycle detection in the nighttime," Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on , vol., no., pp.354,359, 8-11 Oct. 2014
Yi-Ming Chan, Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, and Min-Fang Lo, “Vehicle Detection and Tracking under Various Lighting Conditions Using Particle Filter,” IET Transactions on Intelligent Transportation Systems, 2012.
Bin-Feng Lin, Yi-Ming Chan, Li-Chen Fu, Pei-Yung Hsiao, Li-An Chuang, Shin-Shinh Huang, and Min-Fang Lo, “Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-spot Area,” IEEE Transactions on Intelligent Transportation Systems, 2012.
Shih-Shinh Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「Region-Level Motion-Based Background Modeling and Subtraction Using MRFs,」 IEEE Trans. on Image Processing (SCI) Vol. 16, No. 5, pp. 1446-1456, May 2007.
Shih-Shinh Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「Region-Level Motion-Based Foreground Segmentation under a Bayesian Network,」 Submitted to IEEE Trans. on Circuits and Systems for Video Technology (SCI), Feb. 2007..
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「Region-Level Motion-Based Background Modeling and Subtraction Using MRFs,」 Lecture Notes in Computer Science, 2006.
Yi-Ming Chan, Shih-Shinh Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「Vehicle Detection under Various Lighting Conditions by Incorporate Particle Filter,」 Journal of Vehicle Engineering, Vol.3, pp.31-44 , May 2006.
Jyun-Fan Tsai, Shih-Shinh Huang, Chan-Yu Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「On Road Image Acquiring and Anti-Blooming System at Nighttime by Using High Dynamic Image Reconstruction with Motion Compensation,」 Journal of Vehicle Engineering, Vol.3, pp.45-58, May 2006.
Pei-Yung Hsiao, Hsien-Chein Cheng, Shih-Shinh Huang, and Li-Chen Fu, 「A Mixed Signal CMOS Imager with Lane Detector for Use in Smart Vehicles,」 Submitted to IEEE Trans. on Vehicular Technology (SCI), Oct. 2006.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「Silhouette-Based Human Pose Estimation Using Reversible Jump Markov Chain Monte Carlo,」 IEE Electronics Letters, 2006
Chun-Che Wang, Shih-Shinh Huang, Pei-Yung Hsiao, and Li-Chen Fu, 「Computer Vision-Based Lane Detection and Vehicle Recognition for Driver Assistance System at Night,」 Journal of Vehicle Engineering, Vol.2, pp.51-64 , May 2005.
Robust Dynamic Hand Gesture Recognition System with Sparse Steric Haar-Like Feature for Human Robot Interaction Chengyin Liu, Tzu-Yang Chen, Li-Chen Fu SICE Annual Conference, 2016
Daily Activity Recognition Using the Informative Features from Skeletal and Depth Data Min-Yu Wu, Tzu-Yang Chen, Kuan-Yu Chen, Li-Chen Fu IEEE International Conference on Robotics and Automation, 2016
Deep Learning for Integrated Hand Detection and Pose Estimation Tzu-Yang Chen, Min-Yu Wu, Yu-Hsun Hsieh, Li-Chen Fu International Conference on Pattern Recognition, 2016
Cheng-En Wu, Yi-Ming Chan, Li-Chen Fu, Pei-Yung Hsiao, Shin-Shinh Huang, Han-Hsuan Chen, Pang-Ting Huang and Shao-Chung Hu, "Combining Multiple Complementary Features for Pedestrian and Motorbike Detection," International IEEE Conference on Intelligent Transportation Systems, pp. 1358-1363, 2013
Yi-Ming Chan, Li-Chen Fu, Pei-Yung Hsiao, and Min-Fang Lo, "Pedestrian Detection Using Histograms of Oriented Gradients of Granule Feature," IEEE Intelligent Vehicle Symposium, pp. 1410-1415, 2013.
Yi-Shu Lee, Yi-Ming Chan, Li-Chen Fu, Fellow, IEEE, Pei-Yung Hsiao, Li-An Chuangs, Yi-Hsiang Chen and Ming-Fang Luo, "Nighttime Pedestrian Detection by Selecting Strong Near-Infrared Parts and Enhanced Spatially Local Model," International IEEE Conference on Intelligent Transportation Systems, pp. 1765-1770, 2012.
Yu-Fu Kao, Yi-Ming Chan, Li-Chen Fu, Pei-Yung Hsiao, "Comparison of Granules Features for Pedestrian Detection," International IEEE Conference on Intelligent Transportation Systems, pp. 1777-1782, 2012.
Yu-Chun Lin, Yi-Ming Chan, Luo-Chieh Chuang, Li-Chen Fu, Shih-Shinh Huang, Pei-Yung Hsiao, and Min-Fang Luo, “Near-Infrared Based Nighttime Pedestrian Detection by Combining Multiple Features,” Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 1549-1554, Washington, DC, 2011
Bin-Feng Lin, Yi-Ming Chan, Li-An Chuang, Li-Chen Fu, Pei-Yung Hsiao, and Shih-Shinh Huang, 「Incorporating Appearance and Edge Features for Vehicle Detection in the Blind-Spot Area,」 13th International IEEE Conference on Intelligent Transportation Systems, Madeira Island, Portugal, 2010.
Ssu-Ying Hung, Yi-Ming Chan, Bin-Feng Lin, Li-Chen Fu, Pei-Yung Hsiao, Shin-Shinh Huang, 「Tracking and detection of lane and vehicle integrating lane and vehicle information using PDAF tracking model,」 12th International IEEE Conference on Intelligent Transportation Systems, pp.603-608, 2009
Cheng-Hsiung Chuang, Shih-Shinh Huang, Li-Chen Fu and Pei-Yung Hsaio, 「Monocular Multi-Human Detection Using Augmented Histograms of Oriented Gradients」, IEEE International Conference on Pattern Recognition, 2008.
Chan-Yu Huang, Shih-Shinh Huang, Yi-Ming Chan, Yi-Hang Chiu, Li-Chen Fu and Pei-Yung Hsaio, 「Driver Assistance System Using Integrated Information from Lane Geometry and Vehicle Direction」, IEEE International Conference on Intelligent Transportation Systems, 2007.
Yi-Ming Chan, Shih-Shinh Huang, Li-Chen Fu and Pei-Yung Hsiao, 「Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter」, IEEE International Conference on Intelligent Transportation Systems, 2007.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「A Bayesian Framework for Foreground Segmentation」, IEEE International Conference on Systems, Man, and Cybernetics, 2006.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「Silhouette-Based Human Pose Estimation Using Reversible Jump Markov Chain Monte Carlo,」 IEEE International Conference on Pattern Recognition, 2006.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「A Framework for Human Pose Estimation by Integrating DD-MCMC and MOEA,」 IEEE International Conference on Robotics and Automation, 2006.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「Region-Level Motion-Based Foreground Detection with Shadow Removal Using MRFs,」 Asian Conference on Computer Vision, 2006.
Jyun-Fan Tsai, Shih-Shinh Huang, Yi-Ming Chan, Chan-Yu Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「Road Detection and Classification in Urban Environments Using Conditional Random Field Models,」 IEEE International Conference on Intelligent Transportation System, 2006.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「A Region-Level Motion-Based Background Subtraction Using MRFs,」 IEEE International Conference on Robotics and Automation, 2005.
Chun-Che Wang, Shih-Shinh Huang, Li-Chen Fu, and Pei-Yung Hsiao, 「Driver Assistance System for Lane Detection and Vehicle Recognition with Night Vision,」 IEEE International Conference on Intelligent Robots and Systems, 2005.
Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao, 「A Region-Based Background Modeling and Subtraction Using Partial Directed Hausdorff Distance,」 IEEE International Conference on Robotics and Automation, 2004.
Shih-Shinh Huang, Chung-Jen Chen, Pei-Yung Hsiao, and Li-Chen Fu, 「On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness,」 IEEE International Conference on Robotics and Automation, 2004.
W. C. Hsieh, Li-Chen Fu and Shih-Shinh Huang, 「Vision Based Obstacle Warning System for On-Road Driving,」 IEEE Conference on Intelligent Robots and Systems, 2003.
Z. T. Sun, Li-Chen Fu and Shih-Shinh Huang, 「On-Road Computer Vision Based Obstacle Detection,」 IEEE Conference on Intelligent Robots and Systems, 2002.
C. Y. Liu, Li-Chen Fu, Shih-Shinh Huang, 「Computer Vision Based Objects Detection and Recognition for Safe Vehicle Driving,」 IEEE Conference on Robotics and Automation, 2001.

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