Real-time Object Tracking
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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