利用非视觉标记的持续性动物识别
Persistent Animal Identification Leveraging Non-Visual Markers
AI总结：
针对家笼环境中小鼠视觉特征难区分、遮挡频繁导致视觉跟踪失效的问题，研究结合RFID粗略定位信息，通过整数线性规划求解分配问题并构建轨迹与RFID亲和度概率模型，实现77%准确率的持续性小鼠识别，还可剔除遮挡时的虚假检测。
摘要：
我们的目标是在杂乱的家笼环境中随时间推移定位每只小鼠并为其分配唯一标识符，作为生物学研究中自动化行为识别的前置工作。这是一个极具挑战性的问题，原因在于：（1）每只小鼠缺乏具有区分度的视觉特征；（2）场景空间狭窄且存在持续遮挡，导致标准视觉跟踪方法无法使用。不过，我们可以通过植入的唯一RFID（射频识别）标签获取每只小鼠的粗略位置，因此有望将（弱）跟踪信息与粗略身份信息进行最优融合。为实现目标，我们做出以下核心贡献：（a）将目标识别问题建模为分配问题（采用整数线性规划求解）；（b）提出一种新的概率模型，用于计算轨迹片段与RFID数据之间的亲和度。后者是模型的关键组成部分，因为它为粗略定位下的目标检测提供了有原则的概率处理方法。我们的方法在该动物识别问题上达到77%的准确率，并且能够在动物被遮挡时剔除虚假检测结果。
英文摘要：
Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse's location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), and (b) a novel probabilistic model of the affinity between tracklets and RFID data. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
