As an excellent model organism, zebrafish have been widely applied in many fields. The accurate identification and tracking of individuals are crucial for zebrafish shoaling behaviour analysis. However, multi-zebrafish tracking still faces many challenges. It is difficult to keep identified for a long time due to fish overlapping caused by the crossings. Here we proposed an improved Histogram of Oriented Gradient (HOG) algorithm to calculate the stable back texture feature map of zebrafish, then tracked multi-zebrafish in a fully automated fashion with low sample size, high tracking accuracy and wide applicability. The performance of the tracking algorithm was evaluated in 11 videos with different numbers and different sizes of zebrafish. In the Right-tailed hypothesis test of Wilcoxon, our method performed better than idTracker, with significant higher tracking accuracy. Throughout the video of 16 zebrafish, the training sample of each fish had only 200-500 image samples, one-fifth of the idTracker's sample size. Furthermore, we applied the tracking algorithm to analyse the depression and hypoactivity behaviour of zebrafish shoaling. We achieved correct identification of depressed zebrafish among the fish shoal based on the accurate tracking results that could not be identified by a human.

中文翻译：

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基于改进的HOG功能的自动多斑马鱼跟踪。

作为一种出色的模式生物，斑马鱼已广泛应用于许多领域。个体的准确识别和跟踪对于斑马鱼浅滩行为分析至关重要。但是，多斑马鱼跟踪仍然面临许多挑战。由于过境造成鱼重叠，很难长时间保持识别。在这里，我们提出了一种改进的定向直方图（HOG）算法来计算斑马鱼的稳定后背纹理特征图，然后以低样本量，高跟踪精度和广泛适用性的全自动方式跟踪多斑马鱼。在11个视频中评估了跟踪算法的性能，这些视频具有不同数量和大小的斑马鱼。在Wilcoxon的右尾假设检验中，我们的方法比idTracker的效果更好，具有更高的跟踪精度。在整个16条斑马鱼的视频中，每条鱼的训练样本只有200-500个图像样本，是idTracker样本大小的五分之一。此外，我们应用了跟踪算法来分析斑马鱼浅滩的抑郁和活动不足行为。基于人类无法识别的准确跟踪结果，我们实现了鱼群中抑郁斑马鱼的正确识别。