Object trajectory clustering via tensor analysis

Huiyu Zhou; Dacheng Tao; Yuan Yuan; Xuelong Li

doi:10.1109/ICIP.2009.5414536

Object trajectory clustering via tensor analysis

Huiyu Zhou^*, Dacheng Tao, Yuan Yuan, Xuelong Li

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

In this paper we present a new video object trajectory clustering algorithm1, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.

Original language	English
Title of host publication	2009 16th IEEE International Conference on Image Processing (ICIP)
Publisher	IEEE
Pages	1945-1948
Number of pages	4
ISBN (Print)	9781424456543
DOIs	https://doi.org/10.1109/ICIP.2009.5414536
Publication status	Published - 1 Jan 2009
Event	2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, United Kingdom Duration: 7 Nov 2009 → 10 Nov 2009

Conference

Conference	2009 IEEE International Conference on Image Processing, ICIP 2009
Country/Territory	United Kingdom
City	Cairo
Period	7/11/09 → 10/11/09

Keywords

Clustering
Mean shift
Object trajectory
Tensor analysis

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10.1109/ICIP.2009.5414536

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title = "Object trajectory clustering via tensor analysis",

abstract = "In this paper we present a new video object trajectory clustering algorithm1, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.",

keywords = "Clustering, Mean shift, Object trajectory, Tensor analysis",

author = "Huiyu Zhou and Dacheng Tao and Yuan Yuan and Xuelong Li",

year = "2009",

month = jan,

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doi = "10.1109/ICIP.2009.5414536",

language = "English",

isbn = "9781424456543",

pages = "1945--1948",

booktitle = "2009 16th IEEE International Conference on Image Processing (ICIP)",

publisher = "IEEE",

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note = "2009 IEEE International Conference on Image Processing, ICIP 2009 ; Conference date: 07-11-2009 Through 10-11-2009",

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TY - GEN

T1 - Object trajectory clustering via tensor analysis

AU - Zhou, Huiyu

AU - Tao, Dacheng

AU - Yuan, Yuan

AU - Li, Xuelong

PY - 2009/1/1

Y1 - 2009/1/1

N2 - In this paper we present a new video object trajectory clustering algorithm1, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.

AB - In this paper we present a new video object trajectory clustering algorithm1, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.

KW - Clustering

KW - Mean shift

KW - Object trajectory

KW - Tensor analysis

UR - http://www.scopus.com/inward/record.url?scp=77951956142&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/5414536/

U2 - 10.1109/ICIP.2009.5414536

DO - 10.1109/ICIP.2009.5414536

M3 - Conference publication

AN - SCOPUS:77951956142

SN - 9781424456543

SP - 1945

EP - 1948

BT - 2009 16th IEEE International Conference on Image Processing (ICIP)

PB - IEEE

T2 - 2009 IEEE International Conference on Image Processing, ICIP 2009

Y2 - 7 November 2009 through 10 November 2009

ER -

Object trajectory clustering via tensor analysis

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