Mr. Yixing Su Profile

Yixing Su

at Tsinghua Univ

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Proceedings Article | 18 November 2022 Paper

Decoupling features via global and local contexts in multi-object tracking

Yixing Su, Hongbing Ma, Shengjin Wang

Proceedings Volume 12473, 124731E (2022) https://doi.org/10.1117/12.2653540

KEYWORDS: Convolution, Feature extraction, Video, Visualization, Visual analytics, Systems modeling, Sensors, Performance modeling, Optimization (mathematics), Network architectures

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Multi-object tracking (MOT) system usually consists of two tasks, object detection and re-identification (ReID). Current MOT methods tend to join detection and ReID in a single network to enhance inference speed. Such one-shot models allow joint optimization of detection and Re-ID via a shared backbone, reducing computation cost. However, the different demands of features between the two tasks in one-shot systems lead to competition in the optimization procedure. The detection task needs the features of the instances with the same class to be similar, while the ReID task needs the features of different instances to be distinguishable. Existing methods address the contradiction by disentangling the features into detection-specific and ReID-specific features. But these methods neglect the discussion of semantic interpretation of disentangling modules. In this paper, we propose a feature decoupling module, Global and Local Context-based Decoupling Module (GLCD), to disentangle features extracted by the backbone into two task-specific features. By extracting global and local contexts, the two tasks can choose different contexts by learnable parameters to enforce each self. We conduct our decoupling module into SOTA one-shot MOT method and experiments show performance improvement.

Proceedings Article | 27 January 2021 Paper

Low-budget real-time pose estimation method for personal physical exercise assistant

Zhaodong Bing, Han Wang, Yixing Su, Shengjin Wang

Proceedings Volume 11720, 117200V (2021) https://doi.org/10.1117/12.2589506

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Pose estimation is a fundamental task in the field of computer vision. It contains a huge variety of sub-tasks, including 3D pose estimation, pose tracking, etc. In this paper, we propose a novel algorithm of pose estimation to determine whether it is correct of doing certain physical exercises by single-person pose-tracking and key frame extraction method. First, we use the proposed tracking compensation method to refine the output of pose estimation network. Second, we define different angles composed of human key joints as action angles to determine the standards of physical exercises. Therefore, we can have a personal physical exercise assistant and do physical exercises even without professional personal trainer around. Experiments show that our real-time method can achieve 92.0% accuracy on two kinds of physical exercise actions. It can be adapted to different applications with important significance in theory and practice.

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