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Perception of Human Periodic Motion in Moving Light Displays

AISB: Biologically-Inspired Machine Vision, Theory and Application. Perception of Human Periodic Motion in Moving Light Displays. Baihua Li Horst Holstein Department of Computer Science University of Wales, A berystwyth. Introduction. Moving Light Displays –

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Perception of Human Periodic Motion in Moving Light Displays

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  1. AISB: Biologically-Inspired Machine Vision, Theory and Application Perception of Human Periodic Motion in Moving Light Displays Baihua Li Horst Holstein Department of Computer Science University of Wales, Aberystwyth

  2. Introduction Moving Light Displays – Gait perception in Psychology: • Activity recognition. [Johansson, 1975] • Friends Recognition. [Cutting et al. 1977] • Gender recognition. [Barclay et al. 1978] Researches with MLDs : • Computer vision recognize and classify human activities; lip reading recognition; identify individual subjects. • Biomedical studies, e.g. gait analysis • Motion analysis and synthesis, (game/animation) • kinematics studies, e.g. sportsscience • Humanoid robot design [Hill et al, 2000]

  3. Our approach (in MLDs) Motion-based recognition: We directly use motion information embedded in unidentified feature points for recognition, like suggested by the MLDs. Structure-based recognition: recognition depends on the reconstruction of kinematic structure, using such as joint angles. • Spatio-temporal method: • match portions of feature curves • with template database curves. • scale space or Dynamic Time • Warping techniques for handling • human motion irregularities. Frequency-domain method: We model human periodic movements by feature power spectra using Fourier analysis. Motion recognition is carried out in frequency domain. • avoid structure recovery. • simplify the matching procedure. Computationally expensive! Recognition of human movements

  4. Marker-based optical MoCap system - Vicon 512 (3D-MLDs) Z Y X

  5. 3D-MLDs data collection from Vicon 512 system • 16 feature points • high quality uninterrupted but unidentified 3D-trajectories • sample rate: fs=60 Hz;

  6. Power spectrum: DC component: ( vertical position relative to origin ) Method step I.Power spectral analysis by FFT on vertical-components vertical-component (Z-trajectory) FFT: frequency resolution:

  7. narrow bandwidth: 10Hz • Clustering distribution • around some frequencies • related to the fundamental • activity frequency and its • multiples. • Spectral envelope is • invariant regardless the • start point of FFT. • Power magnitude and • spectral pattern are • reflected by the active • body parts. • Same kind of motion in different subjects, spectral patterns of same feature points are similar, but may different from speed and amplitude. (a) elbow (b) hip (c) knee (d) toe

  8. Gait-cycle (Gc) detection for power spectral normalization A walking Gc=0.88Hz

  9. Power spectral normalization by Gc (a) elbow (b) hip (c) knee (d) toe

  10. Full-body feature-point power spectra scaled by Gc running walking

  11. Method step II.Feature power vector and motion template skipping-on-spot circle-walking running-on-spot

  12. Method step III.Motion recognition Matching matrix M: Similarity parameter: • generate the motion template for an observed motion. • compare it with standard templates by calculating Matching matrices . • The best match is indicated by the maximum Similarity Parameter derived • from Matching matrices.

  13. Standard motion templates Observed activities Circle - walking (clockwise) Circle - walking (anticlockwise) Running -on-spot Skipping -on-spot circle-walking (clockwise) .98 .92 .85 .84 circle-walking (anticlockwise) .93 0.98 .86 .83  walking-on-spot .94 .94  .89 .85 butterfly-walking (in a circle, clockwise) .92 .91 .86 .86 running-on-spot .86 .88  .97 .95 circle-running (clockwise) .89 .87 .95 .93 skipping-on-spot .85 .86  .94 .96 Experimental results

  14. Conclusion • Perception of human common periodic movements from only the reduced information in MLDs can be achieved not only by human, but also machine. • Motion recognition can be carried of by unstructured means without complicated structure recovery. • Modeling and recognition of human periodic movements can be carried out not only in spatio-temporal domain, but also in frequency domain. • Motion characteristics can be extracted from the full-body power spectra on key points, combined with a feature-power analysis. These frequency domain features hint motion nature which can be used to classify different periodic activities, and even discriminate similar movements within the sameclass. • Experimental results indicate the proposed method using MLDs data is successful and efficient for recognizing human periodic motion. Motion analysis from MLDs uses concise and accurate data to investigate what are the essential recognition features for motion analysis. • Subject recognition ?

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