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Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the "drone-vs-bird detection challenge" to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be 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. This paper reports on the challenge proposal, evaluation, and results 1 .

In this paper, we present a method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined bandpass energy filters according to their ability to segregate the different features in the image, thus generating a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is based on a measure of statistical dependence between pairs of frequency features. We will illustrate the applicability of the method to the initialization of a three-dimensional geodesic active model.