  Researchers in natural science need reliable methods for quantifying animal
behavior. Recently, numerous computer vision methods emerged to automate the
process. However, observing wild species at remote locations remains a
challenging task due to difficult lighting conditions and constraints on power
supply and data storage. Event cameras offer unique advantages for
battery-dependent remote monitoring due to their low power consumption and high
dynamic range capabilities. We use this novel sensor to quantify a behavior in
Chinstrap penguins called ecstatic display. We formulate the problem as a
temporal action detection task, determining the start and end times of the
behavior. For this purpose, we recorded a colony of breeding penguins in
Antarctica for several weeks and labeled event data on 16 nests. The developed
method consists of a generator of candidate time intervals (proposals) and a
classifier of the actions within them. The experiments show that the event
cameras' natural response to motion is effective for continuous behavior
monitoring and detection, reaching a mean average precision (mAP) of 58% (which
increases to 63% in good weather conditions). The results also demonstrate the
robustness against various lighting conditions contained in the challenging
dataset. The low-power capabilities of the event camera allow it to record
significantly longer than with a conventional camera. This work pioneers the
use of event cameras for remote wildlife observation, opening new
interdisciplinary opportunities. https://tub-rip.github.io/eventpenguins/
