Mr. Sicong Jiang Profile

Sicong Jiang

at Northeastern 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

SPIE Journal Paper | 26 September 2018

Long-term tracking algorithm using deep features and a single shot multibox detector

Jianing Zhang, Sicong Jiang, Yunzhou Zhang, Xiaobo Liu, Dongdong Wang, Feng Qiu

JEI, Vol. 27, Issue 05, 053019, (September 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.5.053019

KEYWORDS: Detection and tracking algorithms, Sensors, Image filtering, Target detection, Optical tracking, Feature extraction, Electronic filtering, Video, Feature selection, Convolution

Read Abstract +

The ability for computers to target and track objects within video sequences has attracted increasing amounts of attention in recent years. Many tracking algorithms adopt correlation filtering-based algorithms, in which targeting of features in the previous video frame is essential to predicting the target’s position in the next frame. A serious problem in tracking is drift. If drift occurs in the present frame, it becomes increasingly difficult to correct this error in later frames, which can lead to loss of the tracked target and greatly reduced tracking accuracy and stability. To solve this problem and improve tracking performance, we propose an algorithm that incorporates deep features. A sparse representation method is used to filter deep features and improve their complexity. Then, a Siamese network is used to judge the similarities between the target and template and determine a confidence level. Next, a single shot multibox detector (SSD) is introduced to support tracking, which is activated when the confidence level falls below a certain threshold. The correct target can be found by scanning the present frame using the detector and then updating the template. The confidence level is then reduced and the SSD continues working to resolve the drift. Tracking stability and accuracy are greatly improved after the redetection process. We use large-scale benchmark datasets to demonstrate that the proposed approach performs better than existing state-of-the-art approaches.

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