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Real-time Object Tracking
 

Real-time Object Tracking

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Related article: Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain," Proceedings of SPIE, N. ...

Related article: Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain," Proceedings of SPIE, N. Kehtarnavaz and M.F. Carlsohn, San Jose, CA, USA: SPIE, 2009, pp. 72440D-72440D-12.

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    Real-time Object Tracking Real-time Object Tracking Presentation Transcript

    • Real-time Detection and Tracking of Multiple Objects with Partial Decoding in H.264/AVC Bitstream Domain Wonsang You University of Augsburg, Germany Electronic Imaging, 19 January 2009
    • MOTIVATION
      • Real-time Object Detection and Tracking in H.264|AVC Bitstream
    • Pixel Domain Approach
      • Categories of Object Detection and Tracking Approaches.
        • Pixel domain approach
        • Compressed domain approach
      • Pixel domain approach.
        • Using raw pixel data
        • High accuracy
        • High computational complexity
        • Require additional computation for compressed videos
      • Compressed domain approach
        • Exploit encoded information (DCT, motion vectors, etc)
        • Poor performance
          • Applicable for simple scenarios
          • Weak for occlusion
    • Compressed Domain Approach
      • Basic idea
        • Exploit encoded information (DCT, motion vectors, etc)
      • Advantages
        • Remarkably fast processing time
        • Adaptive to compressed videos
      • Disadvantages
        • Unreliability of encoded information
        • Sparse assignment of block-based data
        • Poor performance
          • Applicable for simple scenarios
          • Weak for occlusion
    • Related Works in Compressed Approach
      • Basic Solution
        • Using a low-resolution image from DCT coefficients
        • Unfortunately, impossible for AVC bitstreams
      DC
    • Our Solution for H.264/AVC Bitstreams
      • Basic idea
        • We use partially-decoded pixel data instead of low-resolution images.
      • Advantages
        • Reliable performance in more natural scenes
          • Articulated objects such as humans
          • Objects changing in size
          • Objects which have monotonous color or a chaotic set of motion vectors
        • Occlusion handling
        • Detecting and tracking multiple objects in stationary background
        • Real-time processing
        • Partial decoding in I-frames
          • It has been considered to be impossible
          • Due to spatial prediction dependency on neighboring blocks
    • Overview of the Proposed Algorithm
      • Extraction Phase
        • Probabilistic Spatiotemporal Macroblock Filtering
        • Roughly extracting the block-level region of objects
        • Constructing the approximate object trajectories in each P-frame
      • Refinement Phase
        • Accurately refining the obect trajectories
        • Background subtraction and partial decoding in I-frames
        • Motion interpolation in P-frames
    • EXTRACTION PHASE
      • Real-time Object Detection and Tracking in H.264|AVC Bitstream
    • Probabilistic Spatiotemporal Macroblock Filtering
      • Probabilistic Spatiotemporal Macroblock Filtering
        • Block-based filtering of background parts (BGs)
        • By using spatial and temporal properties of macroblocks
        • Rapid processing of segmenting object regions and tracking each object
    • Block Clustering
      • Block clustering
        • Removing skip macroblocks
        • Eliminating probable background parts
        • Clustering the remaining MBs into several fragments
      • Block group (BG)
        • Set of non-skip blocks
      BGs
    • Spatial Filtering
      • Filtering block groups which are likely to be background
        • Removing BGs of
          • One-macroblock
          • All zero IT coefficients
      • Active Block Group (ABG)
        • Remaining BGs after spatial filtering
      ABGs : Remaining BGs after Spatial Filtering
    • Temporal Filtering
      • Filtering ABGs which are likely to be background
        • Removing ABGs of background
          • Based on temporal consistency of each ABG over a given period
        • Fragments with high occurrence probability: considered as a part of objects
      Remaining ABGs after Temporal Filtering
    • Temporal Filtering
      • Observing occurrence of ABGs during a finite period
        • ABGs with high occurrence for finite period are judged as "Real Object".
        • Occurrence Probability is measured.
      ABGs
    • Temporal Filtering ABGs Criteria for survival of ABG as an object
    • REFINEMENT PHASE
      • Real-time Object Detection and Tracking in H.264|AVC Bitstream
    • Background Subtraction in I-frames Reference Blocks (A-D) are substituted into background image Partial Decoding in I-frames ROI Refinement in I-frames A B C D
    • Motion Interpolation in P-frames Assumption : The object moves slowly nearly with uniform motion in one GOP ROI Refinement in P-frames In the ROI prediction stage, ROI significantly vary over P-frames. So, ROI refinement is needed for P-frames.
    • Occlusion Handling Comparing Hue color histogram of two objects
    • Experimental Results (1/3) Indoor Sequence: 49.5 frames/sec Ourdoor Sequence: 37.12 frames/sec
    • Experimental Results (2/3)
    • Experimental Results (3/3)
    • Thank You! Wonsang You [email_address] University of Augsburg Germany