Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

中文翻译：

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使用关键点检测和MAP估计对动物群进行长期跟踪，以进行个体动物识别。

对于计算机视觉和动物科学研究者而言，在小组环境中跟踪单个动物是一项紧迫的任务。当目标是数月不间断的跟踪并且目标动物的身体特征缺乏明显的差异时，此任务将带来重大挑战。为了解决这些挑战，提出了一种概率检测跟踪方法。跟踪方法使用全卷积检测器提供的单个动物的可见关键点作为输入。个别动物还配备有耳标，分类网络使用这些耳标为实例分配唯一标识。利用目标的固定基数来创建连续的轨迹集，并且使用前向后向算法为每个检测到的实例分配耳标识别概率。跟踪可在消费级硬件上实现实时性能，部分原因是它不依赖复杂，昂贵的基于图形的优化。引入了一个公共的，人工注释的数据集来评估跟踪性能。该数据集包含15个半小时长的不同年龄/大小，设施环境和活动水平的猪的视频。结果表明，所提出的方法在整个数据集中实现了平均精度，并且召回率超过95％。对错误事件的分析表明，在实际部署中最有可能导致错误的环境条件和社会互动。引入了人工注释的数据集以评估跟踪性能。该数据集包含15个半小时长的不同年龄/大小，设施环境和活动水平的猪的视频。结果表明，所提出的方法在整个数据集中实现了平均精度，并且召回率超过95％。对错误事件的分析表明，在实际部署中最有可能导致错误的环境条件和社会互动。引入了人工注释的数据集以评估跟踪性能。该数据集包含15个半小时长的不同年龄/大小，设施环境和活动水平的猪的视频。结果表明，所提出的方法在整个数据集中实现了平均精度，并且召回率超过95％。对错误事件的分析揭示了在实际部署中最有可能导致错误的环境条件和社会互动。