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Representing Honey Bee Behavior for Recognition Using Human Trainable Models

机译：使用人类可训练模型表示蜜蜂行为以进行识别

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摘要

Identifying and recording subject movements is a critical, but time-consuming step in animal behavior research. The task is especially onerous in studies involving social insects because of the number of animals that must be observed simultaneously. To address this, we present a system that can automatically analyze animal movements, and label them, by creating a behavioral model from examples provided by a human expert. Further, in conjunction with identifying movements, our system also recognizes the behaviors made up of these movements. Thus, with only a small training set of hand labeled data, the system automatically completes the entire behavioral modeling and labeling process. For our experiments, activity in an observation hive is recorded on video, that video is converted into location information for each animal by a vision-based tracker, and then numerical features such as velocity and heading change are extracted. The features are used in turn to label the sequence of movements for each observed animal, according to the model. Our approach uses a combination of kernel regression classification and hidden Markov model (HMM) techniques. The system was evaluated on several hundred honey bee trajectories extracted from a 15 minute video of activity in an observation hive.
机译：在动物行为研究中，识别和记录对象的运动是关键但耗时的步骤。由于必须同时观察动物的数量，因此在涉及社交昆虫的研究中，这项任务尤其繁重。为了解决这个问题，我们提出了一个系统，该系统可以通过根据人类专家提供的示例创建行为模型来自动分析动物运动并标记动物运动。此外，结合识别动作，我们的系统还可以识别由这些动作组成的行为。因此，仅使用少量的手工标记数据训练集，系统即可自动完成整个行为建模和标记过程。对于我们的实验，将观察蜂房中的活动记录在视频上，然后通过基于视觉的跟踪器将视频转换为每只动物的位置信息，然后提取速度和航向变化等数字特征。根据模型，这些特征依次用于标记每只观察到的动物的运动顺序。我们的方法结合了内核回归分类和隐马尔可夫模型（HMM）技术。该系统是根据从15分钟的观察蜂房活动视频中提取的数百条蜜蜂轨迹进行评估的。

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