Challenges
1. Drone-vs-Bird Detection Challenge
Angelo Coluccia, Alessio Fascista, Arne Schumann, Lars Sommer, Anastasios Dimou and Dimitrios Zarpalas
Abstract
The challenge aims at attracting research efforts to identify novel solutions to the problem outlined above, i.e., discrimination between birds and drones at far distance, by providing a video dataset that may be difficult to obtain (drone flying require special conditions and permissions, and shore areas are needed for the considered problem). The challenge goal is to detect a drone appearing at some time in a short video sequence where birds are also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. The dataset is continually increased over consecutive installments of the challenge and made available to the community afterwards.
2. Performance Evaluation of Tracking and Surveillance (PETS 2021)
James Ferryman, Roman Pflugfelder and George Melzer-Venturi
Abstract
This challenge introduces a new and exciting surveillance challenge, through-foliage detection and tracking. Such an application is important for (green) border surveillance. The specific tasks addressed are (1) through-foliage detection and fragmented occlusion, and (2) long-term tracking in natural environments, both of which have been received relatively limited attention in the computer vision community. Solutions to solve these tasks are currently unavailable. The aim of this challenge is to raise awareness to these tasks in the vision community and to foster appropriate solutions. The overall border surveillance challenge has significant impact for border authorities worldwide for enhancing border security operations. A corresponding dataset is provided for this challenge which covers the two tasks described above.