School of Technology PUCRS, Brazil.
School of Technology PUCRS, Brazil.
Comput Biol Med. 2018 May 1;96:79-90. doi: 10.1016/j.compbiomed.2018.01.011. Epub 2018 Mar 3.
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.
本文提出了一种半自动的多目标跟踪方法，用于跟踪一群未标记的斑马鱼。该方法可以处理部分遮挡情况，保持每个个体的正确身份。对于每个物体，我们提取了一组几何特征，用于算法的两个主要阶段。第一阶段基于图像中识别的斑点和卡尔曼滤波器实例生成的估计，选择最佳候选者。在第二阶段，如果两个或更多实例选择了相同的候选斑点，则会执行斑点分割算法，以分割该斑点并重新建立实例的身份。如果算法无法确定斑点的身份，则需要手动干预。该程序在具有不同鱼数和空间分辨率的四个视频序列上与手动标记的地面实况进行了比较。然后，将所提出的方法的性能与文献中发现的两种著名的斑马鱼跟踪方法进行了比较：一种方法处理遮挡场景，另一种方法仅跟踪未被遮挡的鱼。基于使用的数据集，所提出的方法在正确分离遮挡中的鱼方面优于第一种方法，在至少 8.15%的情况下提高了其效率。对于第二种方法，在所测试的视频中，所提出的方法在某些视频中的整体性能优于第二种方法，特别是在图像质量较低的情况下，因为第二种方法需要高空间分辨率的图像，而这不是所提出的方法的要求。然而，所提出的方法能够分离遮挡中的鱼，并在高达 87.85%的情况下正确分配其身份，而不考虑用户干预。
