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.

翻译：我们的目标是在杂乱的家笼环境中，通过时间维度为每只小鼠定位并提供唯一标识符，作为生物研究中自动行为识别的前期工作。这是一个极具挑战性的问题，原因在于：(i) 每只小鼠缺乏可区分的视觉特征；(ii) 场景空间狭小且存在持续遮挡，导致标准视觉追踪方法无法使用。然而，通过独特的RFID植入物可获得每只小鼠位置的粗略估计，因此存在将（弱）追踪信息与粗略身份信息进行最优结合的可能性。为实现该目标，我们做出以下关键贡献：(a) 将对象识别问题形式化为指派问题（通过整数线性规划求解）；(b) 提出一种新的轨迹片段与RFID数据之间关联度的概率模型。后者是模型的关键组成部分，它为在粗略定位条件下对对象检测结果进行合理的概率化处理提供了理论依据。该方法在该动物识别问题上达到77%的准确率，并能有效排除动物隐藏时的虚假检测结果。