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Video Trails: Representing and Visualizing Structure in Video Sequences

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Title: Video Trails: Representing and Visualizing Structure in Video Sequences


1
Video Trails Representing and Visualizing
Structure in Video Sequences
  • Vikrant Kobla
  • David Doermann
  • Christos Faloutsos

2
Outline
  • Background and Motivation
  • Overview
  • Video Trails
  • Trail Segmentation
  • Trail Classification
  • Gradual Transition Detection
  • Experiments and Results
  • Conclusion

3
Background and Motivation
  • Video is a valuable information resource
  • There are still few efficient ways to provide
    access to the information the video contains
  • Early work on indexing video treated video
    sequence as collections of still images, ignored
    the temporal structure
  • Efficient analysis and representation of the
    temporal structure of a video is necessary

4
Overview
  • Generate a trail of points (Video Trails) in a
    low-dimensional space
  • Segment the video trails
  • Classify each of those segmented trails into two
    types
  • Stationary (low activity) VS Transitional
    (high activity)
  • 4. Detect gradual transition

5
Video Trails
  • Definition A trail of points in a
    low-dimensional space where each point
    is derived from physical features of a single
    frame in the video clip
  • Features DC coefficients of the luminance and
    chrominance components of an MPEG frame
  • Dimensionality Reduction (FastMap)
  • initial feature vector

  • a vector in that dimensional
  • target dimension
    space

FastMap
6
Example
  • Consider a video clip with a 320x240 frame size
  • Each frame has 20x15 MBs( Macroblock)
  • Each MB contains 6 DC coefficients ( 4 luminance
    and 2 chrominance)
  • Totally, 20x15x61800 coefficients (initial
    vector)
  • 1800-by-1 vector

  • (X1,X2,X3)
  • 3 (target dimension)

FastMap
7
Example
8
Example
9
Trail Segmentation
  • Segment the video in order to determine regions
    of high activity corresponding to transitions and
    low activity corresponding to individual shots
  • The problem of segmenting the video into sets of
    frames is transformed into the problem of
    splitting the video trails into smaller trails
    corresponding to segments of video

10
Splitting Algorithm
  1. Start by placing the first point in a new trail
  2. Consider each successive point in the sequence in
    order
  3. Perform a test for inclusion of this point in
    the current trail
  4. if (the test pass)
  5. Include the point in the current trail
  6. Move to the next point
  7. Goto 2
  8. else
  9. Close the current trail with the previous
    point as the last one
  10. Start a new trail with only the current
    point
  11. Goto 2

11
Inclusion Test
  • Marginal CostTotal cost per point in the trail
  • Consider a clip with N frames
  • Assume there are m points in the current trail,
    denoted by set , and be the point being
    considered for inclusion
  • Define ,d is the
    dimensionality
  • So the new marginal cost is
  • new marginal cost gt previous one not include
  • new marginal cost lt previous one include

12
Example
13
Example (close-up)
14
The sequence of frames that yield the sparse
transition between the two dense clusters
15
Trail Classification
  • Classify each of those segmented trails into
  • Stationary (low activity) or Transitional
    (high activity)
  • Classification Criteria
  • Monotonicity W10.4
  • Sparsity W20.3
  • Convex Hull Volume Ratio W30.2
  • MBR Shape W40.1

16
Monotonicity
  • If a trail is (close to) monotonic, in some
    direction,its likely transitional
    projection of distance along k
  • projected distance ratio

  • the length of MBR dimension k
  • Minimum projected distance ratio

17
Monotonicity (Normalization)
  • Recall
  • W1 is the weight of monotonicity
  • Tlow is the lower bound1.1
  • Tup is the upper bound2.0

18
Sparsity
  • Sparsity total MBR volume per point
  • Average Sparsity
  • Sparsity Ratio
  • Normalize

19
Convex Hull Volume Ratio
  • The ratio of volume of the convex hull of points
    in a trail to the volume of MBR
  • Normalize

20
MBR Shape
  • Cuboidal
  • Planar
  • Elongated

21
Classification
22
Gradual Transition Detection
  • Dissolves, Fades, Wipes
  • Difficulty activity arising from camera or large
    object motion also yields trails similar to
    trails resulting from gradual edits
  • Filter out any kind of global motion leading to a
    transitional trail, Analysis global motion

23
Results
24
Conclusion
  • Provide a compact representation of a video
    sequence structure
  • Reduce a sequence MPEG frames to a trail of
    points in a low dimensional space
  • Segment trails and classify each segment as
    either stationary or transitional
  • Detect gradual edits
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