Tracking data are increasingly used for studying social behavior in dairy cows but guidelines are not available regarding the appropriate system setup and data quality. In this study, we investigated the effects of system calibration, as well as data filtering and smoothing methods on the detection of the location and neighbor preferences of dairy cows in a barn using an ultra-wideband real-time location system (RTLS). We followed a group of 15 or 14 cows during two periods: period 1 with RTLS calibration using a laser distance meter; and period 2 with RTLS calibration using professional surveying technology. During both periods, tracking data were collected every 1.73 s for 3 days for all cows in the group together with data from an electronic feeder system. We performed continuous video analysis of cow locations for 1 day in both periods. We compared the effects of four different filtering and smoothing methods (i.e., Kalman filter, sliding window, jump filter, and median filter) on data preparation using raw tracking data. We assigned specific areas (i.e., lying stalls, walking alley, brush area, feed, and water bins) to the measured X-Y coordinates and compared them with the video-based zone assignments. Sensitivity and precision were calculated to evaluate the quality of the zone assignments corresponding to the different RTLS calibration setups and the effects of filtering and smoothing methods. When the RTLS was accurately calibrated these methods did not result in further improvements. The zone assignments agreed well for the video and all of the prepared tracking data at the lying stalls in both periods, and the agreement was good during period 2 at the feed bunk (sensitivity and precision >0.85). When using zone-based and distance-based approaches for detecting neighbors at the lying stalls and feed bunk based on the prepared tracking data we found high correlations (>0.9) with the neighbor information based on video and electronic bin data in period 2. However, the zone-based approach resulted in the lowest mean absolute error. The quality of the tracking data appeared to be especially important when detecting cows in small areas and with short visit durations (e.g., feed bunk). Our results highlight the importance of accurate RTLS calibration setup and the quality of tracking data when inferring dairy cow behavior based on it.

跟踪数据越来越多地用于研究奶牛的社会行为，但没有关于适当的系统设置和数据质量的指南。在这项研究中，我们使用超宽带实时定位系统 (RTLS) 研究了系统校准以及数据过滤和平滑方法对检测牛舍中奶牛的位置和邻居偏好的影响。我们在两个时期跟踪了一组 15 或 14 头奶牛：时期 1 使用激光测距仪校准 RTLS；以及使用专业测量技术的 RTLS 校准期间 2。在这两个时期，每 1.73 秒收集一次该组中所有奶牛的跟踪数据以及来自电子饲喂系统的数据，持续 3 天。我们对两个时期的奶牛位置进行了 1 天的连续视频分析。我们比较了四种不同的滤波和平滑方法（即卡尔曼滤波器、滑动窗口、跳跃滤波器和中值滤波器）对使用原始跟踪数据的数据准备的影响。我们将特定区域（即卧铺、步行小巷、刷区、饲料和水箱）分配给测量的 XY 坐标，并将它们与基于视频的区域分配进行比较。计算灵敏度和精度以评估对应于不同 RTLS 校准设置的区域分配的质量以及过滤和平滑方法的效果。当 RTLS 被准确校准时，这些方法并没有带来进一步的改进。两个时期的视频和所有准备好的跟踪数据的区域分配都非常一致，并且在第 2 阶段饲料槽中的一致性很好（灵敏度和精密度 >0.85）。当使用基于区域和基于距离的方法来检测基于准备好的跟踪数据的卧铺和饲料铺的邻居时，我们发现与基于视频和电子垃圾箱数据的邻居信息在第 2 阶段具有高度相关性 (>0.9)。然而，基于区域的方法导致最低的平均绝对误差。当在小区域和短访问持续时间（例如，饲料铺）检测奶牛时，跟踪数据的质量似乎特别重要。我们的结果强调了准确的 RTLS 校准设置和基于它推断奶牛行为时跟踪数据质量的重要性。当使用基于区域和基于距离的方法来检测基于准备好的跟踪数据的卧铺和饲料铺的邻居时，我们发现与基于视频和电子垃圾箱数据的邻居信息在第 2 阶段具有高度相关性 (>0.9)。然而，基于区域的方法导致最低的平均绝对误差。当在小区域和短访问持续时间（例如，饲料铺）检测奶牛时，跟踪数据的质量似乎特别重要。我们的结果强调了准确的 RTLS 校准设置和基于它推断奶牛行为时跟踪数据质量的重要性。当使用基于区域和基于距离的方法来检测基于准备好的跟踪数据的卧铺和饲料铺的邻居时，我们发现与基于视频和电子垃圾箱数据的邻居信息在第 2 阶段具有高度相关性 (>0.9)。然而，基于区域的方法导致最低的平均绝对误差。当在小区域和短访问持续时间（例如，饲料铺）检测奶牛时，跟踪数据的质量似乎特别重要。我们的结果强调了准确的 RTLS 校准设置和基于它推断奶牛行为时跟踪数据质量的重要性。基于区域的方法导致最低的平均绝对误差。当在小区域和短访问持续时间（例如，饲料铺）检测奶牛时，跟踪数据的质量似乎特别重要。我们的结果强调了准确的 RTLS 校准设置和基于它推断奶牛行为时跟踪数据质量的重要性。基于区域的方法导致最低的平均绝对误差。当在小区域和短访问持续时间（例如，饲料铺）检测奶牛时，跟踪数据的质量似乎特别重要。我们的结果强调了准确的 RTLS 校准设置和基于它推断奶牛行为时跟踪数据质量的重要性。