构建首个阿尔卑斯山野生动物多视角视频数据集，助力智能行为监测
MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps
- 基于9个相机陷阱采集多视角视频与音频，标注物种与行为
- 包含14小时视频、8.5小时个体轨迹及6135段动物片段用于行为识别
- 提供双任务基准：行为识别与生态事件分析，适配机器学习与生态研究
野生动物监测对生态学和行为学至关重要，尤其在人类活动加剧的背景下。相机陷阱作为以栖息地为中心的传感器，可大规模无扰动采集数据，但缺乏标注视频数据集制约了视频理解模型的发展。为此，我们推出MammAlps，一个来自瑞士国家公园9个相机陷阱的多模态、多视角野生动物行为监测数据集。该数据集包含超过14小时的带音频视频、2D分割图以及8.5小时密集标注的个体追踪数据，涵盖6135段单体动物片段。我们提出首个基于音频、视频和参考场景分割图的层次化多模态动物行为识别基准。此外，还设计第二个面向生态学的基准，从397个多视角长期生态事件（含误触发）中识别活动类型、物种、个体数量和气象条件。这两个任务相辅相成，有助于弥合机器学习与生态学之间的鸿沟。代码与数据已公开于https://github.com/eceo-epfl/MammAlps。
原文摘要 · Abstract (English)
Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video datasets limits the development of powerful video understanding models needed to process the vast amount of fieldwork data collected. To advance research in wild animal behavior monitoring we present MammAlps, a multimodal and multi-view dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. MammAlps contains over 14 hours of video with audio, 2D segmentation maps and 8.5 hours of individual tracks densely labeled for species and behavior. Based on 6135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark using audio, video and reference scene segmentation maps as inputs. Furthermore, we also propose a second ecology-oriented benchmark aiming at identifying activities, species, number of individuals and meteorological conditions from 397 multi-view and long-term ecological events, including false positive triggers. We advocate that both tasks are complementary and contribute to bridging the gap between machine learning and ecology. Code and data are available at: https://github.com/eceo-epfl/MammAlps
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