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

• Published in: Scientific data Vol. 11; no. 1; pp. 15 - 21
• Main Authors: Alansari, Mohamad, Abdul Hay, Oussama, Alansari, Sara, Javed, Sajid, Shoufan, Abdulhadi, Zweiri, Yahya, Werghi, Naoufel
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 639/705/117; Computer engineering; Consent; Data Descriptor; Datasets; Drones; Human subjects; Humanities and Social Sciences; multidisciplinary; Robots; Science; Science (multidisciplinary); Surveillance; Unmanned aerial vehicles
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02810-y

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.