Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena.
Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena.
复制标题
DOI：
10.1371/journal.pone.0084656
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Ikeno H
中科院分区：
文献类型：
--
作者：
Kimura T;Ohashi M;Crailsheim K;Schmickl T;Okada R;Radspieler G;Ikeno H
跟踪动物行为的计算机程序，从而揭示社会性动物的各种特征和机制，是行为学研究的强大工具。由于蜂群中有数千只蜜蜂，个体以很高的物理密度共存，并且很难追踪，除非专门标记，这可能会影响行为。此外，蜜蜂对光有反应，记录必须在特殊的红光条件下进行，蜜蜂的眼睛将其视为黑暗。由此产生的视频图像几乎无法区分。我们开发了一种新算法 K-Track，用于在平坦的实验室场地中跟踪大量蜜蜂。我们的程序实现了三个主要过程：（A）通过对灰度图像进行简单阈值处理来检测对象（蜜蜂）区域，（B）通过大小、形状和时空位置变化来识别个体，以及（C）通过所有电影帧连接已识别个体的质心以产生个体行为轨迹。我们的软件的跟踪性能是根据移动多人工代理和 16 只蜜蜂绕圆形竞技场行走的视频进行评估的。 K-Track 准确地追踪了人工代理和蜜蜂的轨迹。在后一种情况下，K-track 的性能优于 Ctrax（著名的追踪多种动物的软件）。为了详细调查交互事件，我们手动识别了五个交互类别； “交叉”、“接触”、“经过”、“重叠”和“等待”，并检查模型从蜜蜂的相互作用中准确识别这些类别的程度。所有 7 起已发现的故障均发生在竞技场外缘的墙壁附近。最后，K-Track 和 Ctrax 分别成功跟踪了 84 个记录的交互事件中的 77 个和 60 个。 K-Track 识别了平坦表面上的多只蜜蜂，并跟踪它们的速度变化以及与其他蜜蜂的遭遇，表现良好。
A computer program that tracks animal behavior, thereby revealing various features and mechanisms of social animals, is a powerful tool in ethological research. Because honeybee colonies are populated by thousands of bees, individuals co-exist in high physical densities and are difficult to track unless specifically tagged, which can affect behavior. In addition, honeybees react to light and recordings must be made under special red-light conditions, which the eyes of bees perceive as darkness. The resulting video images are scarcely distinguishable. We have developed a new algorithm, K-Track, for tracking numerous bees in a flat laboratory arena. Our program implements three main processes: (A) The object (bee's) region is detected by simple threshold processing on gray scale images, (B) Individuals are identified by size, shape and spatiotemporal positional changes, and (C) Centers of mass of identified individuals are connected through all movie frames to yield individual behavioral trajectories. The tracking performance of our software was evaluated on movies of mobile multi-artificial agents and of 16 bees walking around a circular arena. K-Track accurately traced the trajectories of both artificial agents and bees. In the latter case, K-track outperformed Ctrax, well-known software for tracking multiple animals. To investigate interaction events in detail, we manually identified five interaction categories; ‘crossing’, ‘touching’, ‘passing’, ‘overlapping’ and ‘waiting’, and examined the extent to which the models accurately identified these categories from bee's interactions. All 7 identified failures occurred near a wall at the outer edge of the arena. Finally, K-Track and Ctrax successfully tracked 77 and 60 of 84 recorded interactive events, respectively. K-Track identified multiple bees on a flat surface and tracked their speed changes and encounters with other bees, with good performance.
登录
查看更多内容
影响因子：
8.8
作者：
Barry, Michael J.
通讯作者：
Barry, Michael J.
DOI：
10.1080/13873954.2011.601420
发表时间：
2012-01-01
影响因子：
1.9
作者：
Bodi, Michael;Thenius, Ronald;Crailsheim, Karl
通讯作者：
Crailsheim, Karl
影响因子：
5.4
作者：
Delcourt, Johann;Becco, Christophe;Poncin, Pascal
通讯作者：
Poncin, Pascal
影响因子：
48
作者：
Dankert, Heiko;Wang, Liming;Hoopfer, Eric D.;Anderson, David J.;Perona, Pietro
通讯作者：
Perona, Pietro
影响因子：
48
作者：
de Chaumont, Fabrice;Coura, Renata Dos-Santos;Olivo-Marin, Jean-Christophe
通讯作者：
Olivo-Marin, Jean-Christophe