Adaptive background learning for vehicle detection and spatio temporal tracking

Chengcui Zhang; Shu Ching Chen; Mei Ling Shyu; Srinivas Peeta

doi:10.1109/ICICS.2003.1292566

Adaptive background learning for vehicle detection and spatio temporal tracking

Chengcui Zhang, Shu Ching Chen, Mei Ling Shyu, Srinivas Peeta

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

29 Scopus citations

Abstract

Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.

Original language	English (US)
Title of host publication	ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	797-801
Number of pages	5
ISBN (Electronic)	0780381858, 9780780381858
DOIs	https://doi.org/10.1109/ICICS.2003.1292566
State	Published - 2003
Event	Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, ICICS-PCM 2003 - Singapore, Singapore Duration: Dec 15 2003 → Dec 18 2003

Publication series

Name	ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia
Volume	2

Other

Other	Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, ICICS-PCM 2003
Country	Singapore
City	Singapore
Period	12/15/03 → 12/18/03

ASJC Scopus subject areas

Signal Processing
Media Technology
Computer Networks and Communications

Access to Document

10.1109/ICICS.2003.1292566

Fingerprint Dive into the research topics of 'Adaptive background learning for vehicle detection and spatio temporal tracking'. Together they form a unique fingerprint.

Cite this

Zhang, C., Chen, S. C., Shyu, M. L., & Peeta, S. (2003). Adaptive background learning for vehicle detection and spatio temporal tracking. In ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia (pp. 797-801). [1292566] (ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia; Vol. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICS.2003.1292566

Adaptive background learning for vehicle detection and spatio temporal tracking. / Zhang, Chengcui; Chen, Shu Ching; Shyu, Mei Ling; Peeta, Srinivas.

ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia. Institute of Electrical and Electronics Engineers Inc., 2003. p. 797-801 1292566 (ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, C, Chen, SC, Shyu, ML & Peeta, S 2003, Adaptive background learning for vehicle detection and spatio temporal tracking. in ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia., 1292566, ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, vol. 2, Institute of Electrical and Electronics Engineers Inc., pp. 797-801, Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, ICICS-PCM 2003, Singapore, Singapore, 12/15/03. https://doi.org/10.1109/ICICS.2003.1292566

Zhang C, Chen SC, Shyu ML, Peeta S. Adaptive background learning for vehicle detection and spatio temporal tracking. In ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia. Institute of Electrical and Electronics Engineers Inc. 2003. p. 797-801. 1292566. (ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia). https://doi.org/10.1109/ICICS.2003.1292566

Zhang, Chengcui ; Chen, Shu Ching ; Shyu, Mei Ling ; Peeta, Srinivas. / Adaptive background learning for vehicle detection and spatio temporal tracking. ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia. Institute of Electrical and Electronics Engineers Inc., 2003. pp. 797-801 (ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia).

@inproceedings{21811d85293147fc81406bf38ac6b996,

title = "Adaptive background learning for vehicle detection and spatio temporal tracking",

abstract = "Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.",

author = "Chengcui Zhang and Chen, {Shu Ching} and Shyu, {Mei Ling} and Srinivas Peeta",

note = "Publisher Copyright: {\textcopyright} 2003 IEEE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, ICICS-PCM 2003 ; Conference date: 15-12-2003 Through 18-12-2003",

year = "2003",

doi = "10.1109/ICICS.2003.1292566",

language = "English (US)",

series = "ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "797--801",

booktitle = "ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia",

}

TY - GEN

T1 - Adaptive background learning for vehicle detection and spatio temporal tracking

AU - Zhang, Chengcui

AU - Chen, Shu Ching

AU - Shyu, Mei Ling

AU - Peeta, Srinivas

PY - 2003

Y1 - 2003

N2 - Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.

AB - Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.

UR - http://www.scopus.com/inward/record.url?scp=78649881364&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649881364&partnerID=8YFLogxK

U2 - 10.1109/ICICS.2003.1292566

DO - 10.1109/ICICS.2003.1292566

M3 - Conference contribution

AN - SCOPUS:78649881364

T3 - ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia

SP - 797

EP - 801

BT - ICICS-PCM 2003 - Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific-Rim Conference on Multimedia, ICICS-PCM 2003

Y2 - 15 December 2003 through 18 December 2003

ER -