多摄像头立体视觉系统实现实时事件分析，支持自适应环境学习
Feedback Driven Multi Stereo Vision System for Real-Time Event Analysis
- 融合多个3D摄像头进行全场景重建，提升复杂环境理解能力
- 通过反馈机制获取用户数据，优化事件识别与追踪性能
- 适用于交互系统中的敏感任务，如安全监控与智能通知
2D相机常用于交互系统，而游戏主机等设备虽配备更强的3D相机，但仅适用于短距离深度感知。总体而言，这些相机在大而复杂的环境中可靠性不足。本文提出一种基于3D立体视觉的处理流水线，适用于普通及敏感应用场景，实现鲁棒的场景理解。通过融合多个3D摄像头进行完整场景重建，系统可执行事件识别、目标追踪和通知等多种任务。利用可能的反馈机制，系统可接收环境中用户的输入数据，以学习更优决策或适应全新环境。本文介绍了该流水线的架构，并展示了初步实验结果。最后，提出了下一步迈向生产部署的关键路线图。
原文摘要 · Abstract (English)
2D cameras are often used in interactive systems. Other systems like gaming consoles provide more powerful 3D cameras for short range depth sensing. Overall, these cameras are not reliable in large, complex environments. In this work, we propose a 3D stereo vision based pipeline for interactive systems, that is able to handle both ordinary and sensitive applications, through robust scene understanding. We explore the fusion of multiple 3D cameras to do full scene reconstruction, which allows for preforming a wide range of tasks, like event recognition, subject tracking, and notification. Using possible feedback approaches, the system can receive data from the subjects present in the environment, to learn to make better decisions, or to adapt to completely new environments. Throughout the paper, we introduce the pipeline and explain our preliminary experimentation and results. Finally, we draw the roadmap for the next steps that need to be taken, in order to get this pipeline into production
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