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养笼环境中，随时间对每只小鼠进行定位并赋予其唯一标识，作为生物研究中自动行为识别的前置步骤。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，导致标准视觉跟踪方法无法使用。然而，通过植入的独特RFID芯片可以获得每只小鼠位置的粗略估计，因此有可能将（弱）跟踪信息与粗略的身份信息进行最优组合。为实现这一目标，我们做出了以下关键贡献：（a）将对象识别问题建模为分配问题（通过整数线性规划求解），（b）提出了一种新颖的轨迹片段与RFID数据之间关联关系的概率模型。后者是模型中的关键部分，因为它基于粗略定位，为对象检测提供了原则性的概率处理方法。我们的方法在该动物识别问题上达到了77%的准确率，并且能够在动物隐藏时拒绝虚假检测。