Temporal Segmentation of Egocentric Videos to Highlight Personal Locations of Interest

With the increasing availability of wearable cameras, the acquisition of egocentric videos is becoming common in many scenarios. However, the absence of explicit structure in such videos (e.g., video chapters) makes their exploitation difficult. We propose to segment unstructured egocentric videos to highlight the presence of personal locations of interest specified by the end-user. Given the large variability of the visual content acquired by such devices, it is necessary to design explicit rejection mechanisms able to detect negatives (i.e., frames not related to any considered location) learning only from positive ones at training time. To challenge the problem, we collected a dataset of egocentric videos containing 10 personal locations of interest. We propose a method to segment egocentric videos performing discrimination among the personal locations of interest, rejection of negative frames, and enforcing temporal coherence between neighboring predictions.