Lifelog is a record of one's personal experiences in daily lives. User's location is one of the most common information for logging a human's life. By understanding one's spatial mobility we can figure out other pieces of context such as businesses and activities. With GPS technology we can collect accurate spatial and temporal details of a movement. However, most GPS receivers generate a huge amount of data making it difficult to process and store such data. In this paper, we develop a generic add-on algorithm, feature-first trajectory simplification, to simplify trajectory data in lifelog applications. It is based on a simple sliding window mechanism counting occurrence of certain conditions. By automatically identifying feature points such as signal lost and found, stall, and turn, the proposed scheme provides rich context more than spatio-temporal information of a trajectory. In experiments with a case study of commuting in personal vehicles, we evaluate the effectiveness of the scheme. We find the proposed scheme significantly enhances existing simplification algorithms preserving much richer context of a trajectory.

中文翻译：

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Lifelog应用程序中用于简化轨迹的功能部件。

Lifelog是一个人在日常生活中的个人经历的记录。用户的位置是记录人类生活的最常见信息之一。通过了解一个人的空间流动性，我们可以找出其他背景，例如业务和活动。借助GPS技术，我们可以收集运动的准确时空细节。但是，大多数GPS接收器会生成大量数据，从而难以处理和存储此类数据。在本文中，我们开发了一种通用的附加算法，即功能优先的轨迹简化，以简化生活日志应用程序中的轨迹数据。它基于简单的滑动窗口机制，可计算某些条件的发生。通过自动识别信号丢失和发现，停转和转弯等特征点，所提出的方案比轨迹的时空信息更能提供丰富的上下文。在以个人车辆通勤为例的实验中，我们评估了该方案的有效性。我们发现提出的方案显着增强了现有的简化算法，从而保留了轨迹的更丰富的上下文。