NDLI: Recognizing human-vehicle interactions from aerial video without training

Content Provider	IEEE Xplore Digital Library
Author	Jong Taek Lee Chia-Chih Chen Aggarwal, J.K.
Copyright Year	2011
Description	Author affiliation: Computer & Vision Research Center / Department of ECE, The University of Texas at Austin (Jong Taek Lee; Chia-Chih Chen; Aggarwal, J.K.)
Abstract	We propose a novel framework to recognize human-vehicle interactions from aerial video. In this scenario, the object resolution is low, the visual cues are vague, and the detection and tracking of objects are less reliable as a consequence. Any methods that require the accurate tracking of objects or the exact matching of event definition are better avoided. To address these issues, we present a temporal logic based approach which does not require training from event examples. At the low-level, we employ dynamic programming to perform fast model fitting between the tracked vehicle and the rendered 3-D vehicle models. At the semantic-level, given the localized event region of interest (ROI), we verify the time series of human-vehicle relationships with the pre-specified event definitions in a piecewise fashion. With special interest in recognizing a person getting into and out of a vehicle, we have tested our method on a subset of the VIRAT Aerial Video dataset [11] and achieved superior results. Our framework can be easily extended to recognize other types of human-vehicle interactions.
Starting Page	53
Ending Page	60
File Size	3218454
Page Count	8
File Format	PDF
ISBN	9781457705298
ISSN	21607516
e-ISBN	9781457705304
DOI	10.1109/CVPRW.2011.5981794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Solid modeling Humans Estimation Dynamic programming Vehicles
Content Type	Text
Resource Type	Article

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