Feedback Driven Multi Stereo Vision System for Real-Time Event Analysis

2025-06-22

AI Summary

Existing 2D and short-range 3D cameras exhibit insufficient robustness in large, complex scenes, hindering reliable scene understanding. Method: This paper proposes a feedback-driven multi-view stereo vision framework that fuses data from multiple long-range 3D cameras, integrating depth estimation, real-time scene reconstruction, event recognition, and object tracking—augmented by a user feedback mechanism for adaptive learning. Contribution/Results: The framework overcomes key limitations of conventional 3D perception in far-field, occluded, and dynamic environments. Its end-to-end implementation supports intelligent notification and decision optimization. Extensive experiments across diverse real-world scenarios demonstrate stable event recognition and long-term tracking performance, significantly enhancing environmental adaptability and interaction reliability. The system provides a scalable technical pathway for deploying large-scale intelligent perception systems in practice.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: 3D Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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

Problem

• Enhancing 3D stereo vision for large complex environments
• Fusing multiple 3D cameras for full scene reconstruction
• Improving decision-making via feedback from environment subjects

Innovation

• Multi 3D stereo vision fusion
• Feedback-driven adaptive learning
• Robust scene reconstruction pipeline