Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance

David Montero, Luis Unzueta, Jon Goenetxea, Nerea Aranjuelo, Estíbaliz Loyo, Oihana Otaegui, Marcos Nieto

Abstract: In this paper, we present a cost-effective Video-Surveillance System (VSS) for face recognition and online clustering of unknown individuals at large scale. We aim to obtain Performance Indicators (PIs) for people flow monitoring in large infrastructures, without storing any biometric information. For this purpose, we focus on how to take advantage of a central GPU-enabled computing server, connected to a set of video-surveillance cameras, to automatically register new identities and update their descriptive data as they are re-identified. The proposed method comprises two main procedures executed in parallel. A Multi-Stage Dynamic Batching (MSDB) procedure efficiently extracts facial identity vectors (i-vectors) from captured images. At the same time, an On-Demand I-Vector Clustering (ODIVC) procedure clusters the i-vectors into identities. This clustering algorithm is designed to progressively adapt to the increasing data scale, with a lower decrease in its effectiveness compared t