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 during 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 allows to
record three times longer than with a conventional camera. This work pioneers
the use of event cameras for remote wildlife observation, opening new
interdisciplinary opportunities. this https URL

采用事件相机记录放置在南极的繁殖企鹅群中的数据，使用计算机视觉方法进行行为量化，通过时间动作检测任务确定行为的起始和结束时间，证明事件相机在野生动物观察中的低功耗特性以及对各种光照条件的鲁棒性，为开展新的跨学科研究提供了契机。