NDLI: A statistical approach for trajectory analysis and motion segmentation for freely moving cameras

Content Provider	IEEE Xplore Digital Library
Author	Jiaxin Li Feng Lin Chen, B.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Feng Lin; Chen, B.M.) \|\| Grad. Sch. for Integrative Sci. & Eng., Nat. Univ. of Singapore, Singapore, Singapore (Jiaxin Li)
Abstract	This paper examines the problem of motion segmentation by analyzing trajectories with statistical approach. We propose a statistical framework for motion segmentation, which makes no assumption on camera motion, camera model, number of moving objects and scene complexity. Long range trajectories are traced across frames and clustered by DTW metric. Various descriptors can be used to construct a weighted neighbor graph for the resulted clusters, following by spectral clustering to retrieve trajectories associated with motion. This framework is highly extensive because different descriptors can be combined into the bag-of-features, to build a more accurate neighbor graph to achieve better result. The algorithm is evaluated mainly with the Hopkins 155 database.
Sponsorship	IEEE
Starting Page	001592
Ending Page	001597
File Size	1759481
Page Count	6
File Format	PDF
e-ISBN	9781479917624
DOI	10.1109/IECON.2015.7392328
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Motion segmentation Computer vision Cameras Clustering algorithms Optical imaging Tracking Spectral Clustering Trajectories Weighted Neighbor Graph
Content Type	Text
Resource Type	Article

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