Scientific Data (Jan 2024)

Drone-Person Tracking in Uniform Appearance Crowd: A New Dataset

  • Mohamad Alansari,
  • Oussama Abdul Hay,
  • Sara Alansari,
  • Sajid Javed,
  • Abdulhadi Shoufan,
  • Yahya Zweiri,
  • Naoufel Werghi

DOI
https://doi.org/10.1038/s41597-023-02810-y
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 21

Abstract

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Abstract Drone-person tracking in uniform appearance crowds poses unique challenges due to the difficulty in distinguishing individuals with similar attire and multi-scale variations. To address this issue and facilitate the development of effective tracking algorithms, we present a novel dataset named D-PTUAC (Drone-Person Tracking in Uniform Appearance Crowd). The dataset comprises 138 sequences comprising over 121 K frames, each manually annotated with bounding boxes and attributes. During dataset creation, we carefully consider 18 challenging attributes encompassing a wide range of viewpoints and scene complexities. These attributes are annotated to facilitate the analysis of performance based on specific attributes. Extensive experiments are conducted using 44 state-of-the-art (SOTA) trackers, and the performance gap between the visual object trackers on existing benchmarks compared to our proposed dataset demonstrate the need for a dedicated end-to-end aerial visual object tracker that accounts the inherent properties of aerial environment.