RATS: Robust Automated Tracking and Segmentation of Similar Instances

Continuous identification of objects with identical appearance is crucial to analyze the behavior of laboratory animals. Most existing methods attempt to avoid this problem by excluding direct social interactions or facilitating it by implants or artificial markers. Unfortunately, these techniques may distort the results as they can affect the behavior of the observed animals. In this paper, we present a simple, deep learning-based approach that can overcome these problems by providing reliable segmentation and tracking of similar instances. Recognition of frames where the system could not reliably locate the objects and mark them suggests human supervision is central to the system since there should be no mistake in instance tracking. Manual annotation of these data improves tracking and decreases annotation needs quickly. The proposed method achieves higher segmentation accuracy and more stable tracking than previous methods despite requiring only a small set of manually annotated data.