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居笼环境中，随时间推移定位每只小鼠并为其提供唯一标识符，作为生物研究中自动化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，以及（ii）场景空间狭小且存在持续遮挡，使得标准视觉跟踪方法无法使用。然而，通过植入式RFID芯片可以获取每只小鼠位置的粗略估计，因此存在将（弱）跟踪信息与身份粗略信息优化结合的可能性。为实现这一目标，我们做出以下关键贡献：（a）将对象识别问题表述为分配问题（采用整数线性规划求解），以及（b）一种新颖的轨迹片段与RFID数据之间关联概率模型。后者是模型的关键组成部分，因为它基于粗定位结果，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，并能在动物隐藏时有效排除虚假检测。