Ruiqi Sun

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About

Ruiqi Sun is a researcher at the forefront of computer vision and human–computer interaction, with a primary focus on human action recognition and pose estimation. Their most-cited work, “Human action recognition using a convolutional neural network based on skeleton heatmaps from two-stage pose estimation” (2022, 18 citations), introduces an innovative framework that combines an improved Single Shot Detector (SSD) algorithm with a two-stage pose estimation model. By extracting skeleton data and converting it into heatmaps for convolutional neural network processing, Sun’s approach significantly enhances the accuracy and robustness of action recognition systems. This contribution addresses critical challenges in real-world applications, such as surveillance, healthcare monitoring, and interactive gaming. Sun’s research is notable for bridging the gap between efficient object detection and precise skeletal tracking, offering a scalable solution for dynamic environments. With a growing citation impact, their work is increasingly recognized as a practical advancement in the field, demonstrating how deep learning can be leveraged to interpret complex human movements. For students and researchers exploring action recognition, Sun’s methodology provides a clear, effective pathway for integrating pose estimation with classification tasks.