Fig. 4: Unsupervised reID of animals.
From: Multi-animal pose estimation, identification and tracking with DeepLabCut
a, Schematic of the transformer architecture we adapted to take pose-tensor outputs of the DeepLabCut backbone. We trained it with triplets sampled from tracklets and tracks. b, Performance of the ReIDTransformer method on unmarked fish, mice and marked marmosets. Triplet accuracy (acc.) is reported for triplets sampled from ground truth (GT) tracks and local tracklets only. We used only the top 5% of the most crowded frames, as those are the most challenging. c, Example performance on the challenging fish data. Top: fish-identity-colored tracks. Time is given in frame number. Bottom: example frames (early versus later) from baseline or ReIDTransformer. Arrows highlight performance with ReIDTransformer: pink arrows show misses; orange shows correct ID across frames in ReIDTransformer versus blue to orange in baseline. d, Tracking metrics on the most crowded 5% of frames (30 frames for fish, 744 for marmosets, giving 420 fish targets and 1,488 marmoset targets); computed as described in Methods. IDF1, ID measure, global min-cost F1 score; IDP, ID measure, global min-cost precision; IDR, ID measures: global min-cost recall; Recall, number of detections over number of objects; Precision, number of detected objects over sum of detected and false positives; GT, number of unique objects; MT, mostly tracked and FM, number of fragmentations.
