Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

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Automatic Lesser Kestrel’s Gender Identification using Video Processing

Traditionally, animal surveillance is a common task for biologists. However, this task is often accompanied by the inspection of huge amounts of video. In this sense, this paper proposes an automatic video processing algorithm to identify the gender of a kestrel species. It is based on optical flow and texture analysis. This algorithm makes it possible to identify the important information and therefore, minimizing the analysis time for biologists. Finally, to validate this algorithm, it has been tested against a set of videos, getting good classification results.Junta de Andalucía P10-TIC-570