Indoor Surveillance Robot with Person Following and Re-identification
Snehasis Banerjee, Abhijit Kumar, Apoorv Shekhar · 2024
Can a surveillance robot autonomously raise alerts for anomalous activity from its ego view camera perception in indoor spaces? Can it also follow an un-authorized person to investigate any intrusion? However, there exists a lack of datasets, trained models and methodology to handle surveillance use cases in indoor scenarios. In this work, we have created a hand annotated dataset involving indoor objects, specific to office spaces to understand context of perceived scenes. We demonstrate an end-to-end pipeline to find real time security hazards based on camera perception of a mobile robot to raise appropriate alerts to stakeholders. Additionally, following a person by a mobile robot is an essential feature in the surveillance domain, to check the whereabouts in case the person is a guest or an unauthorized person. While existing work has focused on learning the person model from frontal view, our work has focused on building an online model of a person to follow from any combination of back, side and frontal views. We have specially focused on person re-identification and trajectory, in case the person goes out of view or gets occluded – which is a challenging problem. To address this, we have presented a system and method that learns person’s distinct features on the fly and builds a strategy to navigate while keeping a safe and optimal distance from the person being followed. We also discuss deployment scenarios of the system in a real robot in an office environment.