Real-time tracking of fast-moving object have many important applications in various fields. However, it is a great challenge to track of fast-moving object with high frame rate in real-time by employing single-pixel imaging technique. In this paper, we present the first single-pixel imaging technique that measures zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of a fast-moving object in real time. This method requires only 3 geometric moment patterns to illuminate a moving object in one frame. And the corresponding intensities collected by a single-pixel detector are equivalent to the values of the zero-order and first-order geometric moments. We apply this new approach of measuring geometric moments to object tracking by detecting the centroid of the object in two experiments. The root mean squared errors in the transverse and axial directions are 5.46 pixels and 5.53 pixels respectively, according to the comparison of data captured by a camera system. In the second experiment, we successfully track a moving magnet with a frame rate up to 7400 Hz. The proposed scheme provides a new method for ultrafast target tracking applications.
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Sci Rep
November 2024
School of Artificial Intelligence, Beijing University of Post and Telecommunications, XiTuCheng Road, 10, Haidian, 100082, Beijing, China.
The key object tracking in sports video scenarios poses a pivotal challenge in the analysis of sports techniques and tactics. In table tennis, due to the small size and rapid motion of the ball, identifying and tracking the table tennis ball through video is a particularly arduous task, where the majority of existing detection and tracking algorithms struggle to meet the practical application requirements in real-world scenarios. To address this issue, this paper proposes a combined technical approach integrating detection and discrimination, tailored to the unique motion characteristics of table tennis.
View Article and Find Full Text PDFSci Adv
August 2024
Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.
Real-time tracking and 3D trajectory computation of fast-moving objects is a promising technology, especially in the field of autonomous driving. However, existing image-based tracking methods face significant challenges when it comes to real-time tracking, primarily due to the limitation of storage space and computational resources. Here, we propose a novel approach that enables real-time 3D tracking of a fast-moving object without any prior motion information and at a very low computational cost.
View Article and Find Full Text PDFInt J Public Health
June 2024
College of Architecture and Environment, Sichuan University, Chengdu, Sichuan, China.
Objectives: This study aims to: 1) Explore the mobility experiences of seniors with slow walking speeds (SSWS) in urban neighborhoods; and 2) Investigate their environmental barriers and supports.
Methods: Go-along interviews were conducted with 36 SSWS residing in urban neighborhoods of Chongqing City, China. The mobility patterns and built environment factors influencing their mobility were revealed through cartographic analysis and thematic analysis.
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