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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. Figure 1: Lesser Kestrels (Gray, 2016) (two females and a male). a black sub-terminal band. Conversely, female and younger ones have a more uniform appearance (typical in other common falcon), rusty with black barring and streaking, and being paler underneath. These characteristics are typically exploited by biologists to distinguish visually between males and females of this falcon species more easily. However, breeding behavior study does not refer to an isolated identification. It requires a continuous video observation of the inner nest activity for each individual (distinguishing by gender). Obviously, it is a tedious task, especially for huge amount of videos, in which it is necessary to discard great amount of useless information, with hours of bird inactivity or directly with the empty nest. Additionally, this problem is accentuated for a colony, where this study must be repeated for several nests. Therefore, an automation of this video

Automatic Lesser Kestrel’s Gender Identification using Video Processing