Robust background image generation and vehicle 3D detection and tracking

Yufang Zhang; Peijun Shi; Elizabeth G. Jones; Qiuming Zhu

Robust background image generation and vehicle 3D detection and tracking

Yufang Zhang, Peijun Shi, Elizabeth G. Jones, Qiuming Zhu

Computer Science

Research output: Contribution to conference › Paper › peer-review

10 Scopus citations

Abstract

this research includes two parts: (1) background image generation for vehicle detection, (2) vehicle 3-dimensional (3D) shape recovery and vehicle tracking. In the first part, "Background subtraction" approach is used to detect vehicles in the images. The problem of background image generation is modeled as a mixture of Gaussian distributions and our goal is to separate the background data from other components in the image. A median model is presented as the background image generation method. The second part proposes a size-difference method to recover the vehicle 3D parameters. Vehicle tracking at typical street blocks and intersections is done based on the combination of vehicle features, such as the 3D parameters and pixel intensity statistics.

Original language	English (US)
Pages	12-16
Number of pages	5
State	Published - 2004
Event	Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004 - Washington, DC, United States Duration: Oct 3 2004 → Oct 6 2004

Conference

Conference	Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004
Country/Territory	United States
City	Washington, DC
Period	10/3/04 → 10/6/04

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Computer Science Applications

Cite this

@conference{6381c5366e53477e83967c959177af20,

title = "Robust background image generation and vehicle 3D detection and tracking",

abstract = "this research includes two parts: (1) background image generation for vehicle detection, (2) vehicle 3-dimensional (3D) shape recovery and vehicle tracking. In the first part, {"}Background subtraction{"} approach is used to detect vehicles in the images. The problem of background image generation is modeled as a mixture of Gaussian distributions and our goal is to separate the background data from other components in the image. A median model is presented as the background image generation method. The second part proposes a size-difference method to recover the vehicle 3D parameters. Vehicle tracking at typical street blocks and intersections is done based on the combination of vehicle features, such as the 3D parameters and pixel intensity statistics.",

author = "Yufang Zhang and Peijun Shi and Jones, {Elizabeth G.} and Qiuming Zhu",

year = "2004",

language = "English (US)",

pages = "12--16",

note = "Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004 ; Conference date: 03-10-2004 Through 06-10-2004",

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TY - CONF

T1 - Robust background image generation and vehicle 3D detection and tracking

AU - Zhang, Yufang

AU - Shi, Peijun

AU - Jones, Elizabeth G.

AU - Zhu, Qiuming

PY - 2004

Y1 - 2004

N2 - this research includes two parts: (1) background image generation for vehicle detection, (2) vehicle 3-dimensional (3D) shape recovery and vehicle tracking. In the first part, "Background subtraction" approach is used to detect vehicles in the images. The problem of background image generation is modeled as a mixture of Gaussian distributions and our goal is to separate the background data from other components in the image. A median model is presented as the background image generation method. The second part proposes a size-difference method to recover the vehicle 3D parameters. Vehicle tracking at typical street blocks and intersections is done based on the combination of vehicle features, such as the 3D parameters and pixel intensity statistics.

AB - this research includes two parts: (1) background image generation for vehicle detection, (2) vehicle 3-dimensional (3D) shape recovery and vehicle tracking. In the first part, "Background subtraction" approach is used to detect vehicles in the images. The problem of background image generation is modeled as a mixture of Gaussian distributions and our goal is to separate the background data from other components in the image. A median model is presented as the background image generation method. The second part proposes a size-difference method to recover the vehicle 3D parameters. Vehicle tracking at typical street blocks and intersections is done based on the combination of vehicle features, such as the 3D parameters and pixel intensity statistics.

UR - http://www.scopus.com/inward/record.url?scp=13344275804&partnerID=8YFLogxK

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M3 - Paper

AN - SCOPUS:13344275804

SP - 12

EP - 16

T2 - Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004

Y2 - 3 October 2004 through 6 October 2004

ER -

Robust background image generation and vehicle 3D detection and tracking

Abstract

Conference

ASJC Scopus subject areas

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