Ms. Ming Gong Profile

Ming Gong

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

Proceedings Article | 7 June 2024 Presentation + Paper

YOLO-based GNN for multi-person pose estimation

Ming Gong, Ruixu Liu, Vijayan Asari

Proceedings Volume 13040, 130400F (2024) https://doi.org/10.1117/12.3016568

KEYWORDS: Pose estimation, Object detection, Education and training, Matrices, Spatial resolution, Data modeling, Image processing, Visual process modeling, Semantics, Human computer interaction

Read Abstract +

2D multi-person pose estimation is a well-studied problem for understanding humans in an image. This involves keypoint detection, which requires to detect and localize the points of interest (human joints). Multi-person pose estimation remains challenging because of occlusion of body parts, non-rigidity of human body, variable number of persons in an image and various scales. The most common existing method for keypoint detection is heatmap-based regression. However, there are several drawbacks. The precision relies on the resolution of the output heatmap; the computation is costly for post-process or pre-process for high resolution heatmap; the overlapping heatmap signals of spatial closely keypoints could not be distinguished. Therefore, heatmap-free pose estimation was emerged to tackle these problems. KAPAO and YOLO-Pose are the representations. They both utilized YOLO for keypoint detection since YOLO is an extremely fast object detection method with high accuracy. A graph consists of a collection of nodes and a collection of edges that connect the nodes. A human pose could be referred to a graph, where human joints are nodes and corresponding connection will draw the pose. Graph neural network (GNN) is designed for data with graph structure. Inspired by these, we introduce a YOLO-based GNN, a heatmap-free approach for 2D multi-person pose estimation. YOLO-based network is leveraged for keypoint detection. The detected keypoints and connections will be then re-arranged and refined by GNN. We tested our framework on COCO-2017 dataset and preliminary results show superior performance in accuracy and efficiency.

Proceedings Article | 9 May 2018 Presentation + Paper

Integrated color, shape and texture features for object classification on oil/gas pipeline right-of-ways

Ming Gong, Almabrok Essa, Vijayan Asari

Proceedings Volume 10648, 106480E (2018) https://doi.org/10.1117/12.2305109

KEYWORDS: RGB color model, Feature extraction, Detection and tracking algorithms, Image classification, Binary data, Image analysis, Image processing, Library classification systems, Safety, Sensors

Read Abstract +

Pipeline right-of-way (ROW) monitoring and safety pre-warning is an important way to guarantee a safe operation of oil/gas transportation. Any construction equipment or heavy vehicle intrusion is a potential safety hazard to the pipeline infrastructure. Therefore, we propose a novel technique that can detect and classify an intrusion on oil/gas pipeline ROW. The detection part has been done based on our previous work, where we built a robust feature set using a pyramid histogram of oriented gradients in the Fourier domain with corresponding weights. Then a support vector machine (SVM) with radial basis kernel is used to distinguish threat objects from background. For the classification part, the object can be represented by an integrated color, shape and texture (ICST) feature set, which is a combination of three different feature extraction techniques viz. the color histogram of HSV (hue, saturation, value), histogram of oriented gradient (HOG), and local binary pattern (LBP). Then two decision making models based on K-nearest neighbor (KNN) and SVM classifier are utilized for automatic object identification. Using real-world dataset, it is observed that the proposed method provides promising results in identifying the objects that are present on the oil/gas pipeline ROW.

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