Kwang Moo Yi

Assistant Professor

Research Interests

Computer Vision
Machine Learning
Visual Geometry
Biomedical imaging

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.


Kwang Moo Yi is an assistant professor in the Department of Computer Science at the University of British Columbia (UBC), and a member of the Computer Vision Lab, CAIDA, and ICICS at UBC. Before, he was at the University of Victoria as an assistant professor, where he is currently an adjunct professor. Prior to being a professor, he worked as a post-doctoral researcher at the Computer Vision Lab in École Polytechnique Fédérale de Lausanne (EPFL, Switzerland), working with Prof. Pascal Fua and Prof. Vincent Lepetit. He received his Ph.D. from Seoul National University under the supervision of Prof. Jin Young Choi. He also received his B.Sc. from the same University. He serves as area chair for top Computer Vision conferences (CVPR, ICCV, and ECCV), as well as AAAI. He is part of the organizing committee for CVPR 2023.


Master's students
Doctoral students
Postdoctoral Fellows
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Graduate Student Supervision

Master's Student Supervision (2010 - 2021)
Human pose and stride length estimation (2021)

In this thesis, we develop Computer Vision methods for Human body pose and stride length estimation. We first describe a framework for estimating the stride length of a walking subject from video using a multi-view camera setup. We specifically look into its utility in diagnosing Parkinson's disease. We do this using per frame 3D pose estimates and using an analysis of foot movement, we determine the length of the stride. Parkinson's diagnosis partly relies on stride length information; we claim that our method can be helpful in diagnosis. The current practice in the medical field is to estimate stride with complicated and fundamentally flawed sensors as they tend to affect the gait of the subjects using them. A benefit of our method is that cameras are relatively cheap, easily obtainable, and only need to be set up once. We also describe work done in improving the state of the art in human pose estimation. We first propose a pose refinement method that enhances state-of-the-art methods. Through analysis of our refiner, we show a flaw inherent in the human body model---the inaccuracy in the typical shape-to-pose regressor (joint regressor)---for a standard human pose dataset and show that the results on the top methods are actually being underreported. This flaw results in a situation where the ground truth joints are unsatisfiable with biologically plausible poses. We then address this flaw by modifying a part of the human body model. We reevaluate top state-of-the-art methods and show these models perform better with this modification without retraining.

View record


  • Repurposing existing deep networks for caption and aesthetic-guided image cropping (2022)
    Pattern Recognition, 126, 108485
  • Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems (2021)
    IEEE Transactions on Pattern Analysis and Machine Intelligence,
  • Eigendecomposition-Free Training of Deep Networks with Zero Eigenvalue-Based Losses (2018)
  • Learning to Find Good Correspondences (2018)
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , 2666--2674
  • Learning Lightprobes for Mixed Reality Illumination (2017)
    International Symposium on Mixed and Augmented Reality (ISMAR), (EPFL-)
  • Robust 3D Object Tracking from Monocular Images using Stable Parts (2017)
    IEEE Transactions on Pattern Analysis and Machine Intelligence,
  • Learning to Assign Orientations to Feature Points (2016)
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , 107--116
  • LIFT: Learned Invariant Feature Transform (2016)
    European Conference on Computer Vision,
  • Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model (2016)
    Theory and Applications of Smart Cameras, , 215--240
  • A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images (2015)
    IEEE International Conference on Computer Vision (ICCV), (EPFL-)
  • Authoring and Living Next-Generation Location-Based Experiences (2015)
    proceedings of IEEE Virtual Reality 2015,
  • Category Attentional Search for Fast Object Detection by Mimicking Human Visual Perception (2015)
    Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, , 829--836
  • Development of a Localization System Based on VLC Technique for an Indoor Environment (2015)
  • TILDE: A Temporally Invariant Learned DEtector (2015)
    Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, , 5279--5288
  • Visual tracking in complex scenes through pixel-wise tri-modeling (2015)
    Machine Vision and Applications, 26 (2-3), 205--217
  • Visual tracking of non-rigid objects with partial occlusion through elastic structure of local patches and hierarchical diffusion (2015)
    Image and Vision Computing, 39, 23--37
  • [DEMO] Tracking texture-less, shiny objects with descriptor fields (2014)
    Mixed and Augmented Reality (ISMAR), 2014 IEEE International Symposium on, , 331--332
  • Motion Interaction Field for Accident Detection in Traffic Surveillance Video (2014)
    Pattern Recognition (ICPR), 2014 22nd International Conference on, , 3062--3067
  • Scale preserving PTZ tracking with size estimation using tilt sensory data (2014)
    Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, , 289--294
  • The Visual Object Tracking VOT2014 Challenge Results (2014)
    ECCV 2014 Workshops,
  • Two-stage online inference model for traffic pattern analysis and anomaly detection (2014)
    Machine vision and applications, 25 (6), 1501--1517
  • Detection of moving objects with a moving camera using non-panoramic background model (2013)
    Machine Vision and Applications, 24 (5), 1015â€
  • Multiple ground plane estimation for 3D scene understanding using a monocular camera (2012)
    Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12,
  • Visual tracking with dual modeling (2012)
    Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12,
  • Intelligent visual surveillance — A survey (2010)
    Int. J. Control Autom. Syst., 8 (5), 926â€
  • Orientation and Scale Invariant Kernel-Based Object Tracking with Probabilistic Emphasizing (2010)
    Computer Vision – ACCV 2009, , 130â€
  • PTZ 카메라를 사용한 강인한 물체 추적 (2010)
    대한전자공학회 2010 년 하계종합학술대회, 154̃ 157 쪽 (총 4 쪽),
  • 지능형 영상 감시 알고리즘 개발을 위한 통합 시스템 (2010)
    CICS 2010 정보 및 제어 학술대회 논문집, 109̃ 110 쪽 (총 2 쪽),
  • 탐지 갈라짐과 물체 겹침 상황에 강인한 실시간 다개체 추적 (2010)
    대한전자공학회 2010 년 정기총회 및 추계종합학술대회, 367̃ 368 쪽 (총 2 쪽),
  • 데이터 지연 문제를 해결하기 위한 영상 공유형 감시시스템의 구현 (2009)
    대한전자공학회 2009 년 정기총회 및 추계종합학술대회, 223̃ 224 쪽 (총 2 쪽),
  • 물체 추적 성능 향상을 위한 추적 실패의 검출 방법 (2007)
    2007 CICS 정보 및 제어 학술대회 논문집, 461̃ 462 쪽 (총 2 쪽),

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