Our objective is to track and identify mice in a cluttered home-cage environment, 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 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 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庭笼子里追踪和识别小鼠,作为生物研究自动行为识别的前奏,这是极具挑战性的问题,因为(一) 缺乏对每个鼠的辨别视觉特征,和(二) 场景的距离很近,不断隔绝,使得标准目视跟踪方法无法使用,然而,从独特的RFID植入器中可以对每只鼠的位置作出粗略的估计,因此,有可能最佳地将来自(weak)跟踪的信息与关于身份的粗略信息结合起来。为了实现我们的目标,我们做出了以下关键贡献:(a) 将识别问题表述为指派问题(使用Integer线性编程),以及(b) 轨迹和RFID数据之间的近距离的新概率模型。后者是模型的一个关键部分,因为它提供了一种有原则性的稳定性处理方法,即以粗糙的本地化方式探测物体。我们的方法在这一识别问题上达到了77%的精确度,并且能够在动物隐藏时拒绝虚假的探测。