Title:
Gaussian Process Regression Flow for Analysis of Motion Trajectories
Gaussian Process Regression Flow for Analysis of Motion Trajectories
Author(s)
Kim, Kihwan
Lee, Dongryeol
Essa, Irfan
Lee, Dongryeol
Essa, Irfan
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Abstract
Recognition of motions and activities of objects in videos
requires effective representations for analysis and matching
of motion trajectories. In this paper, we introduce a
new representation specifically aimed at matching motion
trajectories. We model a trajectory as a continuous dense
flow field from a sparse set of vector sequences using Gaussian
Process Regression. Furthermore, we introduce a random
sampling strategy for learning stable classes of motions
from limited data. Our representation allows for incrementally
predicting possible paths and detecting anomalous
events from online trajectories. This representation
also supports matching of complex motions with acceleration
changes and pauses or stops within a trajectory. We
use the proposed approach for classifying and predicting
motion trajectories in traffic monitoring domains and test
on several data sets. We show that our approach works well
on various types of complete and incomplete trajectories
from a variety of video data sets with different frame rates.
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Date Issued
2011-11
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Text
Resource Subtype
Post-print
Proceedings
Proceedings