A video multitracking system for quantification of individual behavior in a large fish shoal: Advantages and limits
A video multitracking system for quantification of individual behavior in a large fish shoal: Advantages and limits
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DOI：
10.3758/brm.41.1.228
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发表时间：
2009-02-01
影响因子：
5.4
通讯作者：
Poncin, Pascal
中科院分区：
文献类型：
--
作者：
Delcourt, Johann;Becco, Christophe;Poncin, Pascal
评估了新的多跟踪系统跟踪大量未标记鱼（最多 100 条）的能力。该系统推断每个人的轨迹，并分析长达几分钟的记录序列。该系统在统计个人跟踪方面非常有效，与跟踪的持续时间相比，个人的身份在短时间内很重要。个体识别率通常大于 99%。当鱼图像不与邻近鱼的图像交叉时，识别效率很高（超过 99%）。当两条鱼的图像合并（遮挡）时，我们认为屏幕上的点具有双重身份。因此，即使每个个体的位置测量不精确，在遮挡期间也不存在识别错误。当这两条合并鱼的图像分开（分离）时，个体识别错误更加频繁，但其在统计个体跟踪中的效果很低。另一方面，在完整的个体跟踪中，个体鱼的身份对于整个轨迹很重要，每个识别错误都会使结果无效。在这种情况下，实验者必须观察程序是否分配了正确的标识，并且当出现错误时，必须编辑结果。这项工作的时间成本并不算太高，因为它仅限于分离事件，仅占个体识别的不到 0.1%。因此，在统计和严格的个体跟踪中，该系统允许实验者通过自动测量个体位置来赢得时间。它还可以分析具有非常大样本的动物群的结构和动态特性，其精度和采样是手动测量无法获得的。
The capability of a new multitracking system to track a large number of unmarked fish (up to 100) is evaluated. This system extrapolates a trajectory from each individual and analyzes recorded sequences that are several minutes long. This system is very efficient in statistical individual tracking, where the individual's identity is important for a short period of time in comparison with the duration of the track. Individual identification is typically greater than 99%. Identification is largely efficient (more than 99%) when the fish images do not cross the image of a neighbor fish. When the images of two fish merge (occlusion), we consider that the spot on the screen has a double identity. Consequently, there are no identification errors during occlusions, even though the measurement of the positions of each individual is imprecise. When the images of these two merged fish separate (separation), individual identification errors are more frequent, but their effect is very low in statistical individual tracking. On the other hand, in complete individual tracking, where individual fish identity is important for the entire trajectory, each identification error invalidates the results. In such cases, the experimenter must observe whether the program assigns the correct identification, and, when an error is made, must edit the results. This work is not too costly in time because it is limited to the separation events, accounting for fewer than 0.1% of individual identifications. Consequently, in both statistical and rigorous individual tracking, this system allows the experimenter to gain time by measuring the individual position automatically. It can also analyze the structural and dynamic properties of an animal group with a very large sample, with precision and sampling that are impossible to obtain with manual measures.