2023/12/06 by Friedhelm Hamann, Hamann, Friedhelm, Suman Ghosh +9 · 1 voice · 3 citations
Engineering · Environmental Science · Neuroscience · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #Artificial intelligence #Camera trap #Classifier (UML) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Ecology #Event (particle physics) #FOS: Computer and information sciences #Fish biology, ecology, and behavior #Neural dynamics and brain function #Power (physics) #Power consumption #Real-time computing #Robustness (evolution) #Wildlife
paper · pdf · doi:10.48550/arxiv.2312.03799
openalex publication_date 2023/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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/