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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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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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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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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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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
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Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Related Experiment Video

Updated: Oct 10, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
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3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene.

Jinyeong Heo1, Yongjin James Kwon1

  • 1Department of Industrial Engineering, Ajou University, Suwon 16499, Korea.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

This study presents a novel method for extracting precise 3D vehicle trajectories at complex crossroads using overlapping multi-camera systems and deep convolutional neural networks (DCNNs). The approach enhances autonomous driving safety by accurately tracking vehicles even with occlusions.

Keywords:
3D bounding box estimation3D trajectory extractioncamera calibrationmulti-object trackingoverlapping multi-camera crossroad scene

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate 3D vehicle trajectory extraction is crucial for autonomous driving, especially in complex environments like crossroads.
  • Challenges include narrow camera angles, vehicle occlusion, and limited physical information from single-camera perspectives.

Purpose of the Study:

  • To propose a robust method for estimating 3D vehicle bounding boxes and extracting trajectories using deep convolutional neural networks (DCNNs) in multi-camera crossroad scenes.
  • To address limitations of single-camera systems for comprehensive traffic scene understanding.

Main Methods:

  • Collected traffic data using overlapping multi-cameras for wide-angle coverage.
  • Estimated and tracked 3D vehicle bounding boxes using DCNNs (YOLOv4, multi-branch CNN) with camera calibration.
  • Extracted 3D trajectories on the ground plane using homography matrix calculations from multi-camera data.

Main Results:

  • The proposed method accurately estimates 3D vehicle bounding boxes and extracts trajectories.
  • Trajectory errors were corrected using linear interpolation and regression, achieving high accuracy compared to ground-truth data.
  • Demonstrated superior accuracy and practicality over existing methods in complex traffic scenarios.

Conclusions:

  • The developed DCNN-based multi-camera approach effectively extracts accurate 3D vehicle trajectories at crossroads.
  • This method offers a practical and reliable solution for enhancing perception systems in autonomous driving applications.