3d Trajectory Tracking
3d Trajectory Tracking - Related Videos
06:50
Preparation and 3D Tracking of Catalytic Swimming Devices
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Cited by 1 •
2016
A method to prepare catalytically active Janus colloids that can "swim" in fluids and determine their 3D trajectories is presented.
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Cited by 15 •
2013
A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.
14:03
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
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Cited by 38 •
2013
The use of a 3D automatic video system that can track individual and groups of zebrafish is described. As application example we explore the effects of the NMDA-receptor antagonist MK-801 on shoals of zebrafish.
04:24
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
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Cited by 3 •
2011
We describe a protocol to observe and analyze cell rolling trajectories on asymmetric receptor-patterned substrates. The resulting data are useful for engineering of receptor-patterned substrates for label-free cell separation and analysis.
01:26
Orthogonal Trajectories
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2026
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
FAQs
Reliable reconstruction depends on pairing each coordinate observation with its time, rather than treating the path as a sequence of positions alone. The resulting time-ordered record allows changes in position to be examined as velocity and other changes in state. This matters because two objects can follow similar spatial paths while differing in how quickly they move through each point.
Cameras, range sensors, and other motion-detection systems provide observations of an object’s location from which its three-dimensional coordinates can be estimated. Their role is not merely to record motion visually, but to supply measurements that can be organized across the x, y, and z axes and matched with time. The resulting record supports quantitative analysis.
The measured path and changing state can be compared with a theoretical model of motion. Agreement may support the model under the experimental conditions, while differences can indicate that forces, constraints, or other aspects of the system need closer examination. In physics, this comparison turns observed movement into evidence for evaluating how a system behaves.
A basic workflow begins by collecting sequential observations of a moving object, recording its coordinates along the x, y, and z axes together with corresponding time information. Those measurements are then reconstructed as a time-ordered trajectory, from which position and velocity changes can be examined. The resulting motion record can be compared with a physical expectation or model.
Applications include projectile motion, particle dynamics, fluid flows, and mechanical systems. The same measurement strategy can therefore support studies of isolated moving objects as well as more complex motion within a flow or mechanism. Its usefulness comes from representing movement quantitatively, allowing researchers to examine paths and changing states across three spatial dimensions.
A tracked trajectory can reveal how an object’s position and velocity change over time, while also showing whether its motion follows an expected path. Researchers can use these observations to identify the influence of forces and constraints and to assess complex movement in an experiment. This provides a basis for comparing measured behavior with theoretical descriptions.