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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15721
Title: Classification of object trajectories represented by high-level features using unsupervised learning
Authors: Saini R.
Ahmed A.
Dogra D.P.
Pratim Roy, Partha
Kumar S.
Raman B.
Roy [initials]P.P.
Sen D.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: Object motion trajectory classification is an important task, often used to detect abnormal movement patterns for taking appropriate actions to prohibit occurrences of unwanted events. Given a set of trajectories recorded over a period of time, they can be clustered to understand usual flow of movement or detection of unusual flow. Automatic traffic management, visual surveillance, behavioral understanding, and sports or scientific video analysis are some of the typical applications that benefit from clustering object trajectories. In this paper, we have proposed an unsupervised way of clustering object trajectories to filter out movements that deviate large from the usual patterns. A scene is divided into nonoverlapping rectangular blocks and importance of each block is estimated. Two statistical parameters that closely describe the dynamic of the block are estimated. Next, these high-level features are used to cluster the set of trajectories using k-means clustering technique. Experimental results using public datasets reveal that, our proposed method can categorize object trajectorieswith higher accuracy when compared to clustering obtained using raw trajectory data or grouped using complex method such as spectral clustering. © Springer Science+Business Media Singapore 2017.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2017), 273- 284
URI: https://doi.org/10.1007/978-981-10-2104-6_25
http://repository.iitr.ac.in/handle/123456789/15721
Issue Date: 2017
Publisher: Springer Verlag
Keywords: Clustering
K-means
Label
Node-no
RAG
Surveillance
Trajectory
ISBN: 9.78981E+12
ISSN: 21945357
Author Scopus IDs: 57190288840
57212475498
35408975400
56880478500
Author Affiliations: Saini, R., IIT Roorkee, Roorkee, India
Ahmed, A., Haldia Institute of Technology, Haldia, India
Dogra, D.P., IIT Bhubaneswar, Bhubaneswar, India
Roy, P.P., IIT Roorkee, Roorkee, India
Corresponding Author: Saini, R.; IIT RoorkeeIndia; email: rajkr.dcs2014@iitr.ac.in
Appears in Collections:Conference Publications [CS]

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