Year: 2025
Type: conference-paper
Abstract: This paper presents the outcomes of the PETS2025 challenge, held in conjunction with AVSS 2025 and sponsored by the EU-funded EURMARS project. The challenge introduces a novel maritime surveillance dataset comprising image sequences captured by diverse multi-altitude, multimodal sensors, reflecting the real-world multi-authority environment. The key tasks include: (1) object detection using various sensors across different platforms (ground-based and low-altitude aerial) and spectral ranges (visible, thermal, ultraviolet (UV), and short-wave infrared (SWIR)); (2) long-term tracking of targets in maritime environments spanning both sea and land; and (3) approximating target geolocations by using sensor imagery and telemetry data. Performance evaluations of results submitted by 12 international participants are discussed. The results show the effectiveness of these submissions and highlight ongoing challenges posed by heterogeneous sensors and complex environments. These challenges emphasise the need to further improve detection, tracking, and geolocation approximation for maritime and coastal surveillance.
Authors: Thanet Markchom, Jonathan Boyle, Lulu Chen, James Ferryman, Matteo Marturini, Stephan Veigl, Andreas Opitz, Andreas Kriechbaum-Zabini, Romaios Bratskas, Anastasios Gkamaris, Dimitris Papachristos, George Leventakis, Wenjun Fan, Hsiang-Wei Huang, Jeng-Neng Hwang, Pyongkun Kim, Kwangju Kim, Chung-I Huang, Kenta Saito, Shunta Kaneko, Kyoko Sudo, Nguyen Thanh Thien, Meng-Yu Kao, Jun-Wei Hsieh, Teepakorn Lilek, Tossapol Pomsuwan, Jinjie Gu, Tianyang Xu, Xuefeng Zhu, Xiaojun Wu, Josef Kittler, Stephanie Stacy, Alfredo Gabaldon, Peter Tu, Sangwon Kim, Dongyoung Kim, Kyoungoh Lee
Institutions: University of Reading, AIT Austrian Institute of Technology GmbH, University of the Aegean, University of Washington, Electronics and Telecommunications Research Institute, National Center for High-Performance Computing, Toho University, University of Information Technology, National Yang Ming Chiao Tung University, National Electronics and Computer Technology Center, Jiangnan University, University of Surrey, General Electric (United States)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
OpenAlex ID: W4414166117