An algorithm to track laboratory zebrafish shoals

In this paper, a semi-automatic multi-object tracking method to track a group of unmarked zebrafish is proposed. This method can handle partial occlusion cases, maintaining the correct identity of each individual. For every object, we extracted a set of geometric features to be used in the two main stages of the algorithm. The first stage selected the best candidate, based both on the blobs identified in the image and the estimate generated by a Kalman Filter instance. In the second stage, if the same candidate-blob is selected by two or more instances, a blob-partitioning algorithm takes place in order to split this blob and reestablish the instances' identities. If the algorithm cannot determine the identity of a blob, a manual intervention is required. This procedure was compared against a manual labeled ground truth on four video sequences with different numbers of fish and spatial resolution. The performance of the proposed method is then compared against two well-known zebrafish tracking methods found in the literature: one that treats occlusion scenarios and one that only track fish that are not in occlusion. Based on the data set used, the proposed method outperforms the first method in correctly separating fish in occlusion, increasing its efficiency by at least 8.15% of the cases. As for the second, the proposed method's overall performance outperformed the second in some of the tested videos, especially those with lower image quality, because the second method requires high-spatial resolution images, which is not a requirement for the proposed method. Yet, the proposed method was able to separate fish involved in occlusion and correctly assign its identity in up to 87.85% of the cases, without accounting for user intervention.