论文标题
在动物园环境中对单个大猩猩的面部识别的数据集和应用程序
A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments
论文作者
论文摘要
我们在布里斯托尔动物园花园（Bristol Zoo Gardens）的7个西部低地大猩猩部队中提出了一个带有5K+面部边界框注释的视频数据集。在此数据集中培训，我们对在动物园环境中面部识别单个大猩猩的任务实施并评估了一条标准的深度学习管道。我们表明，仅使用单个帧时，基本的Yolov3驱动应用程序能够在92％的地图上执行标识。逐条跟踪逐步追踪和身份投票在短轨道上的投票可提高97％的地图。为了促进易于利用来丰富动物园环境的研究功能，我们在data.bris.ac.uk上发布了代码，视频数据集，权重和地面真相注释。
We put forward a video dataset with 5k+ facial bounding box annotations across a troop of 7 western lowland gorillas at Bristol Zoo Gardens. Training on this dataset, we implement and evaluate a standard deep learning pipeline on the task of facially recognising individual gorillas in a zoo environment. We show that a basic YOLOv3-powered application is able to perform identifications at 92% mAP when utilising single frames only. Tracking-by-detection-association and identity voting across short tracklets yields an improved robust performance of 97% mAP. To facilitate easy utilisation for enriching the research capabilities of zoo environments, we publish the code, video dataset, weights, and ground-truth annotations at data.bris.ac.uk.
