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A New Framework for Criteria- based Trajectory Segmentation
Kevin Buchin
Joint work with
Sander Alewijnse, Maike Buchin, Andrea Kölzsch,
Helmut Kruckenberg and Michel Westenberg
September 30, 2013
Stopovers in Geese Migration
Goal
• Delineate stopover sites of migratory geese
• Two behavioural types• stopover• migration flight
• Input: • GPS tracks• expert description of
behaviour
Data
• Spring migration tracks • White-fronted geese• 4-5 positions per day• March – June
• Up to 10 stopovers during spring migration• Stopover: 48 h within radius 30 km• Flight: change in heading <120°
stopover migration flight
Criteria
• Decreasing criteria • Increasing criteria
Within radius 30km
At least 48hAND
Change in heading<120°OR
Within radius 30km
Change in heading<120°
At least 48h
Criteria-based Segmentation
[M. Buchin et al. 2011]
• decreasing criteria
[M. Buchin et al. 2012]
• decreasing criteria• min-duration• few outliers
[Aronov et al. 2013]
• general quadratic time• results on continuous
segmentation
New Framework• decreasing criteria• increasing criteria• approx. outliers• Brownian bridges• near-linear time
Demo 1
Criteria-based Segmentation
[M. Buchin et al. 2011]
• decreasing criteria
[M. Buchin et al. 2012]
• decreasing criteria• min-duration• few outliers
[Aronov et al. 2013]
• general quadratic time• results on continuous
segmentation
New Framework• decreasing criteria• increasing criteria• approx. outliers• Brownian bridges• near-linear time
[Kranstauber et al. 2012]
• dynamic Brownian bridges
• not about segmentation
Segment by diffusion coefficient
Demo 2
• Criteria-based Segmentation to identify behavioural states
• Efficient algorithms for a large class of criteria• Also handles criteria AND Brownian bridges
• Case studies: both criteria-based and Brownian bridges work well
Thanks!
Summary