A Self-Assessment Stereo Capture Model Applicable to the Internet of Things

TLDR

The Internet of Things depends on communication among physical devices, and stereo‑capture sensors are crucial for acquiring information in many applications. This work aims to develop self‑assessment stereo‑capture sensors that use objective evaluation criteria to predict shooting quality in long‑distance applications. The authors design evaluation criteria based on toed‑in and parallel camera configurations to assess stereo‑capture quality. Experiments demonstrate that the criteria accurately predict visual perception of stereo‑capture quality for long‑distance shooting.

Abstract

The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems-toed-in camera configuration and parallel camera configuration-are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.