Ms. Ming Gong Profile

Ming Gong

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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

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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

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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.

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