Ms. Leslie G. Watkins Profile

Leslie G. Watkins

at North Carolina State Univ

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 | 15 April 2008 Paper

Vehicle tracking for urban surveillance

William Roberts, Leslie Watkins, Dapeng Wu, Jian Li

Proceedings Volume 6970, 69700U (2008) https://doi.org/10.1117/12.777307

KEYWORDS: Detection and tracking algorithms, Video, Surveillance, Video surveillance, Filtering (signal processing), Image resolution, Image segmentation, Machine vision, Computer vision technology, Medical imaging

Read Abstract +

Tracking is widely used in a variety of computer vision applications, ranging from video surveillance to medical imaging. The principal goal of tracking is to first identify regions of interest in a scene, and to then monitor the movements or changes of the object through the image sequence. In this paper, we focus on unsupervised vehicle tracking for low resolution aerial images taken from an urban area. Various optical effects have traditionally made this tracking problem very challenging. Objects are often lost in tracking due to intensity changes that result from shadowed or partially occluded regions of an image. Additionally, the presence of multiple vehicles in a scene can lead to mistakes in tracking and significantly increased computation time. We propose a feature-based tracking algorithm herein that will seek to mitigate these limitations. To first isolate vehicles in the initial frame, we apply three-frame change detection to the registered images. Feature points are identified in the labelled regions using the Harris corner criteria. To track a feature point from one frame to the next, we search for the point around a predicted location, determined from the feature's previous motion, that minimizes the sum-of-squared-differences value. Finally, during the course of the image sequence, our algorithm constantly searches for new objects that might have entered the scene. We will demonstrate the success of our tracking approach through experimental considerations.

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