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Visual Analysis of Human/Object Motion

Visual Analysis of Human/Object Motion

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Visual Analysis of Human/Object Motion

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  1. Visual Analysis of Human/Object Motion 2003. 1. 29 Ryu Mi Wyun

  2. Contents • Introduction • Applications • Human Motion Analysis • 3D Object Motion Analysis • Conclusion

  3. Motion Analysis • Most active research topics in CV • Promising applications in many areas • Visual Surveillance, Athletic performance analysis, Virtual Reality etc. • Human Motion analysis • Several large research projects • DARPA on VSAM • Leading international journals about CV

  4. Applications • Character Animations - 자연스러운 동작을 Animation에 응용 • Motion Capture에 의한 정보 전달 • Content-based Image Storage • Motion Capture method • 광학식(Optical Motion Capture) • Camera로 사물에 부착된 Reflective Marker 인식 • 자기식(Magnetic Motion Capture) • 자기장 발생 장치+자기장 측정 센서 • 기계식(Mechanical Motion Capture) • 각 관절에 3축 회전 센서 부착

  5. Applications2 • DoMotion • 작업 자세 동작 분석, 3차원 입출력 및 데이터 송수신 • 게임, 애니메이션 개발과 영화의 특수 효과 • 스포츠 동작 분석, 의료 진단을 위한 동작 분석

  6. Recent Developments In Human Motion Analysis Liang Wang, Weiming Hu, Tieniu Tan National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, People’s Republic of China Pattern Recognition 36, (2003) 585 - 601

  7. Introduction – Human Motion Analysis? Important basis for human motion analysis Recently, received increased attentions

  8. Major Issue1 - Detection • Segmentation regions corresponding to people • Motion Segmentation • Background Subtraction - differencing between current image and a reference image • Statistical Method - by comparing the statistics of the current background model • Temporal Differencing - pixel-wise differencing between some consecutive frames • Optical Flow - Characteristics of flow vectors of moving objects

  9. Major Issue1 - Detection • Segmentation regions corresponding to people 2. Object Classification • Shape-based classification - using different description of shape information - Silhouette-based shape representation • Motion-based classification - using a periodic property of non-rigid motion - similarity-based technique • Shape-based + Motion-based

  10. Major Issue2 - Tracking • To prepare data for pose estimate and action recognition • Model-based Tracking • Stick Figure - combination of line segments linked by joints - recognition of the whole feature • 2-D Contour - analogous to 2-D ribbons or blobs • Volumetric Models - more detail using some 3-D models

  11. Major Issue2 - Tracking • To prepare data for pose estimate and action recognition • Region-based Tracking • By tracking each small blob • Active-Contour-based Tracking • Directly extracting the shape of the subjects • Feature-based Tracking • Using sub-features(points)

  12. Major Issue3 - Behavior Understanding • To Analyze and Recognize motion patterns • General Techniques • Dynamic Time Warping - template-based dynamic programming matching technique • Hidden Markov Models - using a finite set of output probability distribution • Neural Network • Action Recognition • Template Matching – compare with pre-stored action • State-space approaches - define each static posture, - uses certain probabilities to generate connections

  13. Major Issue3 - Behavior Understanding • To Analyze and Recognize motion patterns • Semantic Description • Text-based description • Description of human motion is more complex

  14. Discussion • Further Research • Fast and Accurate Motion Segmentation • Occlusion Handling • 3-D Modeling and Tracking • Use of multiple cameras • Action Understanding • Performance Evaluation

  15. On the Semantics of Visual Behavior, Structured events and Trajectories of human action Shaogang Gong, Jeffrey Ng, Jamie Sherrah Department of Computer Science, University of London, UK Image and Vision Computing 20, (2002) 873 - 888

  16. Learning Semantics • Semantics of Autonomous Visual Events • Visual Events : Changes of meaningful states in the image over time • Modeling dynamic pixels using adaptive Gaussian mixture • Computing temporal pixel-energy – in a busy scene - event detection without segmentation

  17. Learning Semantics2 2. Learning Temporal Structures of pixel-energy-history • Pixel energy history – The long-term models

  18. Modeling Semantics • Bayesian Belief Network (BBNs)

  19. Experiments

  20. 3D Particle Tracking Using an Active Vision J.-C. Noyer, C.Boucher, M.Benjelloun Laboratoire d’Analyse des Systéms du Littoral, Université du Littoral Côte d’Opale, France Pattern Recognition (2003)

  21. Basic Idea • To describe the 3D tracking and motion estimation • Precise evaluation of the object position • Central difficulty lies in the non-linearities of the state equations • EKF(Extended Kalman Filter) : non-linear state estimation method • Linearization of equation • PF(Particle Filtering) • Approximation of the conditional pdf, based on particle sampling • Use of a active sensor (n heterogeneous multi-sensors)

  22. 3D Object Tracking and Motion Analysis • 3D State vector  non-linear state equations (dynamics)  EKF / PF Estimation of the 3D motion and position

  23. Results • 2 Co-located, non-moving cameras, three objects, 36 range