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Motion Object Segmentation, Recognition and Tracking

Motion Object Segmentation, Recognition and Tracking. Huiqiong Chen; Yun Zhang; Derek Rivait Faculty of Computer Science Dalhousie University. Aims. Goal of this research

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Motion Object Segmentation, Recognition and Tracking

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  1. Motion Object Segmentation, Recognition and Tracking Huiqiong Chen; Yun Zhang; Derek Rivait Faculty of Computer Science Dalhousie University

  2. Aims • Goal of this research • To achieve a robust, low-complexity and accurate method for motion segmentation by using perceptual organization principles • Motivation • The role of Perceptual organization in vision is critical to success. • Proposed method: GET based motion segmentation • Applications • Video coding and compression • Video surveillance • Military target detection • Medical Imaging • Traffic Monitoring

  3. GET-based Motion Segmentation:System Architecture

  4. System Data Flow

  5. Original frame GET Map Segmentation result MGET groups Sample 1: Walk Man Sequence

  6. Original frame GET Map MGET groups Segmentation result Sample 2: Express Way Sequence

  7. License Recognition and Tracking • Goal • develop a practical solution to extract license plate of moving vehicles so that the license plate of each vehicle passing by can be identified automatically. • Key idea • combine motion tracking with region detection • use application specific knowledge to guide for the target region detection: region shape, ratio of width to height • use knowledge previously discovered to generate a Region of Interest which focuses tracking to relevant areas.

  8. License Recognition and Tracking (Cont’d) GET feature map Original frame

  9. Original frame Region of Interest License plate License Recognition and Tracking (at night)

  10. Original frame Region of Interest MGETs License Recognition and Tracking (During the Day) License plate

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