NDLI: A clustering approach for detecting moving objects captured by a moving aerial camera

Content Provider	IEEE Xplore Digital Library
Author	DeGol, J. Nam, M.
Copyright Year	2014
Description	Author affiliation: Intell. & Decision Technol., MIT Lincoln Lab., Lexington, MA, USA (Nam, M.) \|\| Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (DeGol, J.)
Abstract	We propose a novel approach to motion detection in scenes captured from a camera onboard an aerial vehicle. In particular, we are interested in detecting small objects such as cars or people that move slowly and independently in the scene. Slow motion detection in an aerial video is challenging because it is difficult to differentiate object motion from camera motion. We adopt an unsupervised learning approach that requires a grouping step to define slow object motion. The grouping is done by building a graph of edges connecting dense feature keypoints. Then, we use camera motion constraints over a window of adjacent frames to compute a weight for each edge and automatically prune away dissimilar edges. This leaves us with groupings of similarly moving feature points in the space, which we cluster and differentiate as moving objects and background. With a focus on surveillance from a moving aerial platform, we test our algorithm on the challenging VIRAT aerial data set and provide qualitative and quantitative results that demonstrate the effectiveness of our detection approach.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	6538
Ending Page	6542
File Size	243570
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854864
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Trajectory Computer vision Motion detection Conferences Vehicles Pattern recognition graph representation Aerial video slow motion detection clustering
Content Type	Text
Resource Type	Article

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