Cohabitation Discovery via Spatial and Temporal Clustering

Abstract

Recent years have witnessed the rapid development of deep learning in various aspects, such as image classification, face recognition, and object detection. Yet, these approaches often focus on a single entity. The relationship between different entities is remained to be explored. Cohabitation is a kind of important relationship. In a scenario of residential entries, knowing the relationship of cohabitation could a) prevent tailgaters; b) identify unregistered strangers, and especially c) prevent the disease from spreading during the Cov-19 period. In this paper, we propose a method combining computer vision with graph algorithms to discover the cohabitation relationships as long-term and regular co-occurrence. We demonstrate the method beneficial to both industrial and technical aspects.