Environmental Change Detection in Wearable Video

Abstract

Many people would like to record their daily lives and control their experiences effectively. For this purpose, we have developed a wearable video system to capture our personal experiences. However, segmentation and retrieval of our experiences in the wearable video system is still a significant problem. If used in our everyday lives, a wearable video system records many different scenes in various locations. One of the segmentation points is when we change location from one place to another. We propose a method for enabling a wearable video system to detect scene changes. The proposed method has two stages: in the first stage, the system detects changes in physiological data (heat flux and skin temperature) in order to extract candidate scenes that are then analyzed for environmental changes in the second stage, in which we improve the ability of the system to detect changes in scene by analyzing histogram changes in video frames. In our experiments, the accuracy of detecting environmental changes was 62% in the first stage, which rose to 70% in the second stage, while the recall rate remained above 80%