Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300350, China.
State Key Laboratory of Medicinal Chemical Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
Sci Rep. 2018 Jul 18;8(1):10884. doi: 10.1038/s41598-018-29185-0.
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.
作为一种优秀的模式生物，斑马鱼已被广泛应用于多个领域。准确识别和跟踪个体对于分析斑马鱼的群体行为至关重要。然而，多斑马鱼跟踪仍然面临许多挑战。由于交叉导致的鱼重叠，长时间保持识别状态非常困难。在这里，我们提出了一种改进的方向梯度直方图（HOG）算法，用于计算斑马鱼稳定的背部纹理特征图，然后以低样本量、高跟踪精度和广泛适用性的方式自动跟踪多只斑马鱼。我们在 11 个不同数量和大小的斑马鱼视频中评估了跟踪算法的性能。在威尔科克森右尾检验中，我们的方法比 idTracker 表现更好，具有显著更高的跟踪精度。在 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 样本量的五分之一。此外，我们将跟踪算法应用于分析斑马鱼群体的抑郁和活动不足行为。我们根据无法被人类识别的准确跟踪结果，实现了对鱼群中抑郁斑马鱼的正确识别。
