Efficient Fall Detection Based on Event Pattern Matching in Image Streams

Abstract

This work proposes sliding window fall detection match (SW-FDM), a rule-based fall detection method based on event pattern matching from human body posture event streams. Fall and post-fall (long lie) rules are expressed as patterns, and complex event processing (CEP) systems are adopted to quickly find these patterns. They can be detected with event selection strategies such as Skip Till Next Match and Skip Till Any Match. However, existing strategies generate either duplicate or missing alarms; even worse, their processing cost is very high when the size of event streams is large. Since SW-FDM uses a concept of sliding window, it is able to detect correct matches constantly and reduce the processing cost without duplicate computation. The experiments demonstrate that SW-FDM results in both higher accuracy and efficiency. Also, it is shown that the improvement of efficiency becomes greater as the data size increases, which is an indeed preferable property.