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

## TL;DR

This paper presents a method to distinguish drones from birds using trajectory patterns and deep learning, even when visual details are limited.

## Contribution

A novel trajectory-based classification method using LSTM networks and synthetic data for drone-bird distinction in low-visibility scenarios.

## Key findings

- The model reliably separates drone and bird motion patterns using synthetic trajectory data.
- Trajectory cues alone can enable early drone detection when appearance-based methods fail.
- The method generalizes well to real-world conditions by focusing on motion patterns rather than appearance.

## Abstract

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions.