The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehicles, robot) using such trajectory data with rich spatiotemporal information to facilitate informed strategic and operational decision making. In this study, we consider the task of classifying the activities of moving objects from their noisy indoor trajectory data in a collaborative manufacturing environment. Activity recognition can help manufacturing companies to develop appropriate management policies, and optimise safety, productivity, and efficiency. We present a semi-supervised machine learning approach that first applies an information theoretic criterion to partition a long trajectory into a set of segments such that the object exhibits homogeneous behaviour within each segment. The segments are then labelled automatically based on a constrained hierarchical clustering method. Finally, a deep learning classification model based on convolutional neural networks is trained on trajectory segments and the generated pseudo labels. The proposed approach has been evaluated on a dataset containing indoor trajectories of multiple workers collected from a tricycle assembly workshop. The proposed approach is shown to achieve high classification accuracy (F-score varies between 0.81 to 0.95 for different trajectories) using only a small proportion of labelled trajectory segments.
翻译:日益广泛使用有位置意识的传感器使得能够在不同应用领域收集大量轨道数据; 机器学习使得能够利用丰富的时空信息研究移动物体(如人、车辆、机器人)的活动或行为,利用这种轨迹数据,利用丰富的时空信息来研究移动物体(如人、车辆、机器人)的活动或行为,以便利知情的战略和业务决策; 在这项研究中,我们认为,在合作制造环境中,对移动物体的活动进行分类,使其不受室内噪音轨道数据的影响; 活动识别有助于制造公司制定适当的管理政策,优化安全、生产率和效率; 我们提出了一个半监督的机器学习方法,首先采用信息理论标准,将长轨线分割成一系列部分,使物体在每一段内表现出同质行为; 然后,根据受限制的等级组合方法,对这些部分自动进行标签; 最后,对以动态神经网络为基础的深层学习分类模型进行了轨迹段和生成的假标签的培训; 对包含从三周期组合讲习班收集的多个工人的室内轨迹的数据集进行了评价。 拟议的方法显示,只有使用高精确度分类(F-0.85至0.81轨道段之间)才达到高精确度。