Synchronization of oscillations for machine perception of gaits

Substantial evidence supports a relationship between gait perception and gait synthesis. Furthermore, passive mechanical systems demonstrate that the jointed leg systems of humans have innate oscillations that form a gait. These observations suggest that systems may perceive gaits by synchronizing an internal oscillating model to observed oscillations. We present such a system in this paper that uses phase-locked loops to synchronize an internal oscillator with oscillations from a video source. Arrays of phase-locked loops, called video phase-locked loops, synchronize a system with the oscillations in pixel intensities. We then test the perception of the resulting synchronized-oscillator model in various gait recognition tasks. Tools based on Procrustes analysis and directional statistics provide the computational mechanism to compare patterns of oscillations. We discuss the possibility of an alternative model for motion perception based on synchronization with the transient oscillations of temporal band-pass filters that is consistent with other proposed models for human perception. Synchronization of a kinematic model to oscillations also suggests a path to bridge the gap between the model-free and model-based domains.

[1]  Shaogang Gong,et al.  On the binding mechanism of synchronised visual events , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[2]  David C. Hogg,et al.  Learning Flexible Models from Image Sequences , 1994, ECCV.

[3]  J.E. Boyd,et al.  Phase in model-free perception of gait , 2000, Proceedings Workshop on Human Motion.

[4]  Robert T. Collins,et al.  Silhouette-based human identification from body shape and gait , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[5]  Roland E. Best Phase-locked loops : design, simulation, and applications , 2003 .

[6]  H. Nagel,et al.  Tracking of persons in monocular image sequences , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[7]  Fang Liu,et al.  Finding periodicity in space and time , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Andy Ruina,et al.  An Uncontrolled Toy That Can Walk But Cannot Stand Still , 1997, physics/9711006.

[9]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[10]  R. Jain,et al.  Estimation of articulated motion using kinematically constrained mixture densities , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[11]  Svetha Venkatesh,et al.  Temporal PDMs for gait classification , 2002, Object recognition supported by user interaction for service robots.

[12]  Mark S. Nixon,et al.  Automatic Gait Recognition by Symmetry Analysis , 2001, AVBPA.

[13]  A. B. Drought,et al.  WALKING PATTERNS OF NORMAL MEN. , 1964, The Journal of bone and joint surgery. American volume.

[14]  James M. Rehg,et al.  Analyzing articulated motion using expectation-maximization , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Bir Bhanu,et al.  Bayesian-based performance prediction for gait recognition , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[16]  Sudeep Sarkar,et al.  Baseline results for the challenge problem of HumanID using gait analysis , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[17]  Michael N. Shadlen,et al.  Synchrony Unbound A Critical Evaluation of the Temporal Binding Hypothesis , 1999, Neuron.

[18]  Jeffrey E. Boyd Video Phase-Locked Loops in Gait Recognition , 2001, ICCV.

[19]  Linda B. Smith,et al.  A dynamic systems approach to development: Applications. , 1993 .

[20]  D. G. Albrecht,et al.  Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification , 1997, Visual Neuroscience.

[21]  M. P. Murray Gait as a total pattern of movement. , 1967, American journal of physical medicine.

[22]  Larry S. Davis,et al.  EigenGait: Motion-Based Recognition of People Using Image Self-Similarity , 2001, AVBPA.

[23]  Terence C. Mills,et al.  The Art of Statistical Science: A Tribute to G. S. Watson , 1992 .

[24]  Larry S. Davis,et al.  View-invariant Estimation of Height and Stride for Gait Recognition , 2002, Biometric Authentication.

[25]  A. N. Rajagopalan,et al.  Gait-based recognition of humans using continuous HMMs , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[26]  Mark S. Nixon,et al.  Extended Model-Based Automatic Gait Recognition of Walking and Running , 2001, AVBPA.

[27]  Chuan Yi Tang,et al.  A 2.|E|-Bit Distributed Algorithm for the Directed Euler Trail Problem , 1993, Inf. Process. Lett..

[28]  Gyöorgy Benedek,et al.  A Dynamic Systems Approach to Development: Applications , 1994, Journal of Cognitive Neuroscience.

[29]  J. van Leeuwen,et al.  Audio- and Video-Based Biometric Person Authentication , 2001, Lecture Notes in Computer Science.

[30]  David C. Hogg,et al.  Generating Spatiotemporal Models from Examples , 1995, BMVC.

[31]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[32]  Edward H. Adelson,et al.  Synchrony does not promote grouping in temporally structured displays , 2001, Nature Neuroscience.

[33]  Ralph Gross,et al.  The CMU Motion of Body (MoBo) Database , 2001 .

[34]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Paul R. Cohen,et al.  Empirical methods for artificial intelligence , 1995, IEEE Expert.

[36]  Hany Farid,et al.  Temporal synchrony in perceptual grouping: a critique , 2002, Trends in Cognitive Sciences.

[37]  Ken-ichi Anjyo,et al.  Fourier principles for emotion-based human figure animation , 1995, SIGGRAPH.

[38]  Mark S. Nixon,et al.  Automatic extraction and description of human gait models for recognition purposes , 2003, Comput. Vis. Image Underst..

[39]  Tieniu Tan,et al.  A new attempt to gait-based human identification , 2002, Object recognition supported by user interaction for service robots.

[40]  Murray Mp,et al.  Gait as a total pattern of movement. , 1967 .

[41]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[42]  Larry S. Davis,et al.  Motion-based recognition of people in EigenGait space , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[43]  Larry S. Davis,et al.  Person identification using automatic height and stride estimation , 2002, Object recognition supported by user interaction for service robots.

[44]  M. Coleman,et al.  The simplest walking model: stability, complexity, and scaling. , 1998, Journal of biomechanical engineering.

[45]  Tieniu Tan,et al.  Fusion of static and dynamic body biometrics for gait recognition , 2004, IEEE Trans. Circuits Syst. Video Technol..

[46]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[47]  Tad McGeer,et al.  Passive walking with knees , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[48]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .

[49]  Aaron F. Bobick,et al.  A Multi-view Method for Gait Recognition Using Static Body Parameters , 2001, AVBPA.

[50]  Randal C. Nelson,et al.  Detection and Recognition of Periodic, Nonrigid Motion , 1997, International Journal of Computer Vision.

[51]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[52]  Tad McGeer,et al.  Passive Dynamic Walking , 1990, Int. J. Robotics Res..

[53]  Daniela Perani,et al.  How can virtual reality be? a PET study of motor actions observation , 1999 .

[54]  Aaron F. Bobick,et al.  Gait recognition using static, activity-specific parameters , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[55]  Marius Usher,et al.  Visual synchrony affects binding and segmentation in perception , 1998, Nature.

[56]  Thomas F. Shipley,et al.  Detecting Animals in Point-Light Displays , 2000 .

[57]  Sudeep Sarkar,et al.  Experiments on gait analysis by exploiting nonstationarity in the distribution of feature relationships , 2002, Object recognition supported by user interaction for service robots.

[58]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[59]  James W. Davis,et al.  Visual Categorization of Children and Adult Walking Styles , 2001, AVBPA.

[60]  J. Little,et al.  Recognizing People by Their Gait: The Shape of Motion , 1998 .

[61]  R Blake,et al.  Visual form created solely from temporal structure. , 1999, Science.

[62]  Chiraz Ben Abdelkader Motion-Based Recognition of People in EigenGait Space , 2002 .

[63]  L. Kaufman,et al.  Handbook of perception and human performance , 1986 .

[64]  Stefano Soatto,et al.  Recognition of human gaits , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[65]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[66]  Gene H. Golub,et al.  Matrix computations , 1983 .

[67]  James J. Little,et al.  Motion from Transient Oscillations , 2001 .

[68]  Aaron F. Bobick,et al.  Gait recognition from time-normalized joint-angle trajectories in the walking plane , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[69]  Larry S. Davis,et al.  Robust periodic motion and motion symmetry detection , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[70]  Mark S. Nixon,et al.  Zernike Velocity Moments for Description and Recognition of Moving Shapes , 2001, BMVC.

[71]  P. Bennett,et al.  Generalized Common Fate: Grouping by Common Luminance Changes , 2001, Psychological science.

[72]  Jeffrey E. Boyd,et al.  OSCILLATING MODELS FOR PERCEPTION OF HUMAN MOTION , 2003 .

[73]  Eugene Fiume,et al.  Limit cycle control and its application to the animation of balancing and walking , 1996, SIGGRAPH.

[74]  David C. Hogg,et al.  Learning Spatiotemporal Models From Training Examples , 1995 .

[75]  M. Coleman,et al.  An Uncontrolled Walking Toy That Cannot Stand Still , 1998 .

[76]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.