Fig. 3

Method parameters and performance levels. (A) Timelapse of keypoint-labeled frames of a mouse engaged in circling behavior. (B) Parameter distributions and associated exponential and Gaussian fits from two sample videos. To accommodate the substantial variability observed across videos, we relied on a two-step process of Gaussian kernel estimation followed by fitting to a weighted sum of an exponential and normal distribution. This allowed the same technique to account for differences in e.g. average duration (left column, compare blue Gaussian fits) or greater numbers of small collisions likely to be false positives (right column, compare pink exponential fits). (C) Illustration of circle detection using each of the described methods. Duration-Only considers only time taken to complete the putative circle, Time-Angle additionally calculates the angle of the tail-to-snout vector for each frame and considers its total net change, and Box-Angle removes duration requirements and instead constraints the geometry of the circle based on the axes of a rectangle bounding the candidate circling instance. (D) Examples of false-positive detections using each method. There are clear features which indicate an instance should be filtered out for the Duration-Only (minimal head movement relative to the tail) and Time-Angle (oblong or missized snout path geometry) methods.