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Orthogonal Trajectories01:26

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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...
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Related Experiment Video

Updated: Feb 14, 2026

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Distinguishing a Drone from Birds Based on Trajectory Movement and Deep Learning.

Andrii Nesteruk1,2, Valerii Nikitin1,2, Yosyp Albrekht1

  • 1Faculty of Informatics and Computer Engineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 37 Peremohy Ave., 03056 Kyiv, Ukraine.

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|February 13, 2026
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Summary

Distinguishing drones from birds at long range is crucial for airspace safety. This study uses motion trajectory analysis with a deep learning model to reliably differentiate unmanned aerial vehicles (UAVs) from birds, even when they appear as few pixels.

Keywords:
computer visiondrone detectioninformation technologieslong short-term memory (LSTM) networksmotion trajectory analysissynthetic datasetsunmanned aerial vehicles (UAVs)

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Unmanned aerial vehicles (UAVs) and birds share low-altitude airspace, necessitating reliable identification for safety and security.
  • Traditional appearance-based recognition fails in long-range scenarios where targets are small and lack detail.

Purpose of the Study:

  • To develop a robust method for distinguishing between drones and birds in long-range aerial surveillance.
  • To leverage motion trajectory analysis as a complementary cue to appearance-based detection.

Main Methods:

  • A model-driven simulation pipeline generated synthetic data of multicopter, fixed-wing UAV, and bird trajectories.
  • A bidirectional long short-term memory (LSTM) network was trained on time-series image-plane coordinates and apparent size to classify motion patterns.
  • The model analyzed trajectory characteristics like smoothness, turning behavior, and velocity fluctuations.

Main Results:

  • The LSTM network successfully classified drone-like and bird-like motion patterns on synthetic data.
  • Motion-trajectory cues alone enabled reliable separation of drones from birds, even with scarce visual details.
  • The method demonstrated generalization capabilities for real-world surveillance systems.

Conclusions:

  • Motion-trajectory analysis is an effective method for early distinguishing drones from birds in challenging long-range scenarios.
  • The proposed simulation and classification pipeline provides a reproducible testbed for trajectory-based recognizers.
  • This approach offers a complementary and robust signal for integrated surveillance solutions, independent of appearance-based limitations.