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Using Lifelines For SpatioTemporal Summaries

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spatiotemporal data reduction through the identification of important instances/frames ... spatiotemporal generalization using variable resolution as dictated ... – PowerPoint PPT presentation

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Title: Using Lifelines For SpatioTemporal Summaries


1
Using Lifelines For SpatioTemporal Summaries
  • Anthony Stefanidis, Panos Partsinevelos, Peggy
    Agouris
  • Dept. of Spatial Information Engineering
  • National Center for Geographic Information
    Analysis
  • University of Maine

2
Our Digital Government Project
  • Title Knowledge Management Over Time Varying
    Geospatial Datasets
  • Academic Partners
  • University of Maine (lead)
  • Penn State University
  • University of California - Riverside
  • Duration Aug. 2000 - July 2003

3
Agency/Industry Partners
  • Direct
  • National Imagery Mapping Agency (NIMA)
  • National Agricultural Statistics Service (NASS)
  • BAE Systems
  • Indirect
  • Open GIS Consortium (OGC)
  • US Army Topographic Engineering Center (TEC)
  • Federal Geographic Data Committee (FGDC)

4
Project Objective
  • The development of novel meta-information
    structures conveying the content of time varying
    geospatial dataset collections, in order to
    improve access to and enable the analysis of such
    information

5
Paper Objective
  • The automatic generation of video summaries.
  • Providing support for
  • spatiotemporal data reduction through the
    identification of important instances/frames
  • spatiotemporal generalization using variable
    resolution as dictated by information content
  • spatiotemporal behavioral analysis

6
  • Lifelines S-T trajectories
  • Need Generalization of S-T trajectories through
  • selection of representative frames

7
  • Object Trajectories
  • as Paths in Multidimensional S-T Spaces

8
SOM Extraction of a Curvilinear Medial Axis
9
SOM Extraction (cont.)
Convergence
Ordering achieved
10
Input Imagery
Noisy MLC Results
HYDICE Test Area 1
11
SOM In SpatioTemporal Data
  • S-T Nodes
  • correspond to video frames (or datasets)
  • of nodes generalization degree
  • captures spatial and temporal behavior

12
S-T vs. S data
  • S-T trajectories differ fundamentally from roads
    in that they are not as smooth or regular
  • We need a better generalization approach to
    minimize information loss and redundancy

13
Standard SOM Generalization
  • Problems
  • Makes use of a preselected of nodes
  • Handles poorly geometrically complex areas

14
A New Hybrid SOM
  • Analysis of geometric complexity of generalized
    trajectory
  • Introduction of mini-SOM solutions in complex
    data pockets
  • Removal of redundant nodes
  • Zooming-in in complex instances and zooming-out
    otherwise

15
Single object
Capture spatial and temporal variations turns,
ac/deceleration
16
Outline
t(2) t(1)
tracking
S-T trajectories
Area of interest
Proximity
Topology
Video dataset
SOM
Patches of moving object
Averaging, New Video
Node registration
Rough generalization
t
Thinning
t
x
y
Geometric-SOM generalization
S-T Summary
17
Geometry-SOM Technique
Thinning
Densification
18
Geometry-SOM
19
Generalization Detail
20
Accuracy Measures
  • SOM vs. Hybrid

             
RMS(SOM) 89.3 RMS(Hybrid) 8.9
21
S-T SOM Nodes
  • Instances where the object
  • Accelerated/decelerated
  • Changed orientation

22
Additional S-T Nodes
  • Analyzing other spatial properties of
  • single objects or relations among
  • multiple objects
  • Topology
  • Proximity
  • User-defined

23
Additional Nodes
Proximity
Reasoning
Area of Interest
24
Proximity Experiments
25
Data Handling

s (or t)
O1 O2 O3 O4
26
Lifeline Resolution
Attributes
Scale
Navigate through hierarchical structure in
information content and volume
27
Summary Generation
  • Integrating S-T nodes
  • SOM
  • proximity
  • reasoning
  • Registration and grouping
  • as average (titj)/2
  • as video segment from t(I) to t(j)

28
Supported Queries
How similar are the S-T trajectories of two
objects?
Total Attribute Similarity (Partial Attribute
Similarity Indices)
3-D similarity
29
Find all objects for which a specific attribute
varied in a specific way over a certain extent.
Query increment
O1
O2
Object1 53 Object2 75
30
Scale Independent Comparisons
  • Differential registration
  • Lifeline stretching to reveal hidden relations.

31
Event Similarity Matching
  • Crime rate
  • Pollution

Urban Growth
Relations
32
To Be Continued
For more information http//www.spatial.maine.
edu/peggy/dgi.html or tony_at_spatial.maine.edu
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