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Robust Methods for the Analysis of Images and Videos for Fisheries Stock Assessment Summary of a Workshop (2015) / Chapter Skim
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5 Multi-Object Tracking
Pages 36-42

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From page 36...
... , with presentations by Margrit Betke (Boston University) , Mubarak Shah, Jules Jaffe (University of California, San Diego)
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From page 37...
... Betke explained that her group's tracking approach couples object detection with position estimation and data association (Wu et al., 2012)
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From page 38...
... Betke explained that she and her students also visited the New England Aquarium to record fish with their three-camera visible-light high-speed video recording system; at this point they had merely conducted census studies, which were fairly accurate: the number of fish counted in each of the three camera views mostly matched the number of fish known to be in the tank. Betke concluded by stressing the importance of computer vision in collect ing and analyzing multi-camera video data sets.
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From page 39...
... In a later discussion session, Shah noted that there appears to be a gap between what the fisheries community is doing in the areas of sensors, 3D, and metadata relative to what is traditionally done in computer vision, and, thus, there may be opportunities for future work and improvements in the approaches used for fish eries stock assessments. TRACKING IN THE OCEAN, VEHICLES, AND FISH Jules Jaffe, University of California, San Diego Jules Jaffe began by stating that fish are highly maneuverable and capable of behavior that is not common in many other contexts.
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From page 40...
... SHAPE- AND BEHAVIOR-ENCODED TRACKING Ashok Veeraraghavan, Rice University Ashok Veeraraghavan described three projects that are peripherally related to fisheries tracking: 2D tracking in cluttered environments, 3D tracking of multiple small targets, and small baseline tracking using light-field cameras. His research
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From page 41...
... Veeraraghavan explained that the tracking algorithms used were very simple: background sub traction to remove variations in the background, connected component analysis to link pixels belonging to the same target, and probabilistic data association. The points at which the bees turn in flight (i.e., the points of maximal 2D curvature)
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From page 42...
... He was able to demonstrate that 3D trajectories of fish targets can be extracted, even when one fish occludes another.
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Key Terms

  • shah explained
  • mubarak shah
  • margrit betke
  • fourier analysis
  • stock assessment

  • computer vision
  • jules jaffe
  • ashok veeraraghavan
  • rice university
  • boston university

  • shah noted
  • data association
  • tracking approach
  • occlusion reasoning
  • central florida

  • work encompasses
  • data tend
  • hundred bees
  • buoys spread
  • collect visible

  • markov model
  • fusing information
  • vision applied
  • veeraraghavan explained
  • overall crowd

  • weight release
  • crowd tracking
  • track identifier
  • veeraraghavan described
  • cameras recorded


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