Prof. Guoyou Wang Profile

Prof. Guoyou Wang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 7 March 2024 Paper

A multi-object tracking method combining high-order modeling, future position feature revision, and conceptual features

Kang Luo, Xinfang Zhang, Guoyou Wang, Jinxuan Zhu

Proceedings Volume 13085, 130850L (2024) https://doi.org/10.1117/12.3005358

KEYWORDS: Modeling, Detection and tracking algorithms, Mathematical optimization, Computer vision technology

Read Abstract +

Multiple Object Tracking (MOT) which is an important research topic in computer vision, plays an important role in the fields of automatic driving and area monitoring. In the object dense scene, there is a phenomenon of occlusion between a large number of object. A large number of locally visible object lead to the degradation of the tracking performance of the multi-object tracking algorithm in this scene. In this paper, we propose a method that combines high-order modeling, future location feature revision, and conceptual features to address the missing object re-matching problem. Higher-order modeling enables more accurate approximations of actual functions. It modifies the current prediction with one or more features of future locations. The overall feature of one object is composed of multiple local conceptual features. The object can be expressed by the combination of several concept features when it is greater than a certain similarity threshold. Experimental results show that the above three optimization mechanisms can effectively alleviate the problems of multiobject tracking algorithms in dense object scenes, and the optimized algorithm has significantly improved accuracy in multiple tracking scenarios.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years