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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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相关实验视频

Updated: Sep 19, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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ORT:用于多个对象的跟踪,具有阻塞强度.

Shoudong Han1, Hongwei Wang1, En Yu1

  • 1National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Fundamental research
|June 18, 2025
PubMed
概括

这项研究引入了一个新的多对象跟踪 (MOT) 框架,以解决长期的闭塞问题. 它使用新的预测和关联模块来提高轨迹的准确性和纯度,而无需外观数据.

关键词:
数据关联数据的关联.长期封闭模型的建模.基于运动的预测.多对象跟踪多对象跟踪轨道重新连接的轨迹.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 多对象跟踪 (MOT) 与长期的遮蔽作斗争,在这种情况下,外观特征变得不可靠.
  • 像卡尔曼波器这样的现有方法在闭塞期间的长期运动预测中失败.
  • 在MOT中的数据关联容易发生身份切换,因为在遮蔽期间的得分相似.

研究的目的:

  • 开发一个强大的MOT框架,有效地处理长期的对象封闭.
  • 改进轨迹重新连接,减少隐蔽场景中的身份切换.
  • 为了增强隐蔽对象的运动预测和数据关联.

主要方法:

  • 提出了使用空间和时间变压器进行状态估计和可见性预测的长期空间时间预测 (LSTP) 模块.
  • 引入了一个有序关联 (OA) 模块,该模块根据封闭属性和活跃状态优先考虑轨迹关联.
  • 开发了一个包含LSTP和OA模块的Occlusion-Robust Tracker (ORT) 框架.

主要成果:

  • ORT框架显著缓解了多对象跟踪中的长期封闭问题.
  • 在MOT17和MOT20基准上取得了竞争性表现,超过了最先进的追踪器.
  • 证明了卓越的轨迹准确性和纯度,即使不依赖外观信息.

结论:

  • 拟议的LSTP和OA模块有效地解决了阻塞所带来的运动预测和数据关联挑战.
  • 该ORT框架提供了一个强大的解决方案,用于多对象跟踪在具有显著阻塞的场景.
  • 这种方法通过在具有挑战性的条件下提高跟踪稳定性和可靠性来提高MOT能力.