Paper ID | COVID-IP-2.1 | ||
Paper Title | Cohabitation Discovery via Spatial and Temporal Clustering | ||
Authors | Ruizhe Liu, Onewo Spacetech Service Ltd, China | ||
Session | COVID-IP-2: COVID Related Image Processing 2 | ||
Location | Area A | ||
Session Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation | Poster | ||
Topic | COVID-Related Image Processing: COVID-related image processing | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
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. |