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Coarse-to-Fine Pedestrian Localization and Silhouette Extraction for the Gait Challenge Data Sets

Coarse-to-Fine Pedestrian Localization and Silhouette Extraction for the Gait Challenge Data Sets. Haiping Lu , K.N. Plataniotis and A.N. Venetsanopoulos The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto. Motivation.

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Coarse-to-Fine Pedestrian Localization and Silhouette Extraction for the Gait Challenge Data Sets

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  1. Coarse-to-Fine Pedestrian Localization and Silhouette Extraction for the Gait Challenge Data Sets Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  2. Motivation • The HumanID gait challenge data sets • Semi-automatic extraction • Assuming paths are smooth to 2nd degree • Background subtraction algorithms • Pixel-based processing: not robust • MRF-based processing: exploiting spatial & temporal dependencies but slow • Objective: fast & robust IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  3. Overview IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  4. Silhouette extraction difficulties Heavy shadow Other subjects in the scene Slow motion (eight successive frames) IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  5. Coarse detection • Coarse region Rc: centered at previous Bf • Gray map M1: maximum pixel difference • Foreground pixels F1: threshold Td • Spatial distribution examination: foreground F2 and binary map M2 IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  6. Coarse detection • Bounding box: centered at • Validation and variation control: Bc IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  7. Fine detection • BSMT on Rf (centered at Bc): Sr • Background update (GMM) • Fine detection Bf: projections of Sr and cluster analysis • Rfc : Rf centered horizontally • Final silhouette Sf: Sr within Rfc IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  8. Initial detection • No coarse detection • Whole frame as Rf • Confidence on localization: number of foreground pixels in Bf exceeds 50 IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  9. Experiments • 285 sequences: gallery & probes B, D, H and K of USF gait challenge data sets • 630 frames per sequence on average • Frame: color image of size 480x720 • Parameters determined experimentally IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  10. Pedestrian localization results • 50 frames to gain confidence on localization • 0.07% of 165,749 frames in error (foreground pixel number & dislocation) • Errors: lower portion cut or missing & incomplete silhouettes • Useful for further processing: e.g. LDM IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  11. Silhouette extraction results • Evaluation: resemblance between extracted silhouettes & manual silhouettes (10005 frames available) • Metric: ratio of intersection to union • Consistently better than USF semi-auto extraction & MIT silhouette refinement IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  12. Performance comparison IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  13. Conclusions • Attack difficulties in gait recognition due to slow motion, heavy shadow, other moving subjects or objects • Coarse detection: locate subject • Fine detection: robust and accurate detection • Future work: using models to help silhouette extraction IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  14. Related work • Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "A Layered Deformable Model for Gait Analysis", in Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR 2006), Southampton, UK, April 2006. IEEE International Conference on Multimedia & Expo, Toronto, July 2006

  15. Contact Information Haiping Lu Email: haiping@dsp.toronto.edu Academic website: http://www.dsp.toronto.edu/~haiping/ IEEE International Conference on Multimedia & Expo, Toronto, July 2006

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