Abstract
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.
Highlights
Lifelog is a record of a person’s daily life in varying amounts of detail [1,2,3]
Each data point basically contains object ID, longitude x, latitude y, and time stamp t. It has other information dm, which a GPS receiver provides by nature
From the graph we can see that DP significantly outperforms uniform sampling (US), as we expected, with respect to perpendicular Euclidean distance (PED)
Summary
Lifelog is a record of a person’s daily life in varying amounts of detail [1,2,3]. It represents the totality of life experience and one can potentially improve work performance or find unconscious behavior through self-tracking. Batch algorithms require an entire trajectory data before doing any simplifying operations These generally achieve a good balance between accuracy and storage size at the cost of higher computation. On the other hand, work for streaming trajectory data in real-time applications These generally cannot achieve optimal results by trying to maintain the relatively important data points within a local buffer of restricted size. In order to identify feature points the proposed scheme utilizes additional information such as GPS status, speed, track angle, etc., which a GPS receiver naturally provides, in addition to the location and timestamp information Since it is based on a simple sliding window mechanism its processing complexity and local storage requirements are very low.
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