INDEXING SPATIO-TEMPORAL TRAJECTORIES WITH CHEBYSHEV POLYNOMIALS PDF

DOI : Agrawal, C. Faloutsos, and A. Swami , Efficient similarity search in sequence databases , Proc. Assent, R.

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We'd like to understand how you use our websites in order to improve them. Register your interest. This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations.

Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations — least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform DFT.

Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner.

Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets — Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Aghbari Z. IEEE Trans. Multimedia 5 4 : — Alon J. Bashir F. ACM Multimedia Syst. Buzan D. Chan K. Chang S. Circuits Syst. Video Technol. Chen L. Cui Y. Dagtas S. Image Proc. Faloutsas C. Ranganathan M. Hsieh J. Hsu C. Man Cybern. C 34 3 : — Vehicular Tech. B, 34 3 : — Ivo F. Jain A. Prentice Hall, Englewood Cliffs.

Google Scholar. Jeannin S. Jin Y. Johnson N. Image Vis. Johnson R. Prentice-Hall, New Jersey. KDD archive [Online]. Keogh E. Data Mining and Knowledge Discovery. Khalid S. Kohonen T. Springer, Berlin Heidelberg New York. Naftel A. Owens J. Rea N. Shim C. Vlachos M. Wang L. Pattern Recogn.

Yacoob Y. Image Underst. Download references. Correspondence to Andrew Naftel. Reprints and Permissions. Naftel, A. Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Systems 12, — Download citation. Published : 19 September Issue Date : December Search SpringerLink Search.

Abstract This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Immediate online access to all issues from Subscription will auto renew annually. References 1. You can also search for this author in PubMed Google Scholar. View author publications. Rights and permissions Reprints and Permissions.

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Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page. Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment.

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We'd like to understand how you use our websites in order to improve them. Register your interest. This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations.

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