Augmented UAV Dataset

Overview

This project presents dataset and augmentation work for UAV-based visual detection and tracking.

Problem Statement

UAV-based detection and tracking research depends on data diversity. Limited or overly clean datasets can hide model failures under clutter, motion, small object scale, and weak visual evidence.

Motivation

Robust perception requires varied training and evaluation scenarios. Dataset construction and augmentation are important for testing whether models generalize beyond narrow visual conditions.

Methodology

The work focuses on constructing and augmenting visual UAV data for detection and tracking experiments. Release and usage details are tied to the associated paper and repository.

Key Contributions

• Dataset construction for UAV-based visual detection and tracking.
• Data augmentation support for broader training and evaluation conditions.
• Benchmark-oriented framing for reproducible UAV perception experiments.

Experimental Setup

Input modality: vision-based UAV imagery / video data. Task: UAV detection and tracking dataset construction and evaluation support.

Quantitative Results

See the paper for the full quantitative evaluation.

My Role

My contributions include dataset design, augmentation strategy, benchmark framing, implementation support, analysis, and paper preparation.

Related Publication

M. H. Rahman and S. Madria. "An Augmented Dataset for Vision-Based Unmanned Aerial Vehicles Detection and Tracking." IEEE AIPR, 2023.