2017/09/18 by Maedeh Aghaei, Aghaei, Maedeh, Mariella Dimiccoli +5
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1709.05775
openalex publication_date 2017/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes a system for automatic social pattern characterization using a wearable photo-camera. The proposed pipeline consists of three major steps. First, detection of people with whom the camera wearer interacts and, second, categorization of the detected social interactions into formal and informal. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task, and a LSTM network is employed for time-series classification. In the last step, recurrences of the same person across the whole set of social interactions are clustered to achieve a comprehensive understanding of the diversity and frequency of the social relations of the user. Experiments over a dataset acquired by a user wearing a photo-camera during a month show promising results on the task of social pattern characterization from egocentric photo-streams.