Mr. Daqian Liu Profile

Daqian Liu

SPIE Involvement:

Author

Publications (2)

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 | 19 October 2018

Location-matching tracking under convolutional neural network

Daqian Liu, Wanjun Liu, Bowen Fei

JEI, Vol. 27, Issue 05, 053043, (October 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.5.053043

KEYWORDS: Convolutional neural networks, Network architectures, Target recognition, RGB color model, Video, Particle filters, Statistical modeling, Statistical analysis, Detection and tracking algorithms, Fermium

Read Abstract +

Traditional trackers are easily affected by uncertain changes in tracking targets, such as occlusion, deformation, and background clutter. To solve these problems, we propose a tracking method, namely location-matching tracking under a convolutional neural network (CNN), which consists of a process of localization, recognition, and model updating. In the location subprocess, the target’s locations of the previous (first) frame and the current frame are utilized to estimate a series of specific regions by the average displacement, and these locations are proven to be useful to improve the probability of a successful tracking. In the recognition subprocess, a CNN is adopted to classify the estimated regions, and we calculate the confidence score maps of these regions to estimate the final target region. To improve the accuracy of the tracking, we propose an optimal similarity matching to verify the final target region and make a confidence decision to update the network. Compared with the state-of-the-art trackers on challenging object tracking benchmark benchmarks, the proposed method can achieve the same or even higher tracking accuracy.

Proceedings Article | 15 November 2017 Paper

Image defog algorithm based on open close filter and gradient domain recursive bilateral filter

Daqian Liu, Wanjun Liu, Qingguo Zhao, Bowen Fei

Proceedings Volume 10605, 106053T (2017) https://doi.org/10.1117/12.2295866

Read Abstract +

To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what’s more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.

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