Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey

2025-10-10

AI Summary

To address the fragmentation and lack of synergy among tracking, re-identification (Re-ID), and action understanding (AU) in multi-view, multi-camera (MVMC) systems, this paper proposes the first unified connected vision framework. Moving beyond isolated single-view modeling, the framework integrates multi-view spatiotemporal modeling, continual learning, and privacy-preserving federated learning to jointly enable cross-camera object association, persistent identity recognition, and semantic action parsing. Rigorous evaluation on large-scale multimodal datasets and comprehensive metrics demonstrates robustness and adaptability under occlusion, viewpoint variation, and dynamic environments. Key contributions include: (1) a novel MVMC multi-task collaborative taxonomy; (2) a joint learning paradigm supporting continual model evolution and end-to-end privacy protection; and (3) a systematic analysis of open challenges, outlining future research directions for connected vision systems—particularly concerning computational efficiency, generalization across domains, and real-world deployability.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Connected Vision Systems (CVS) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human-robot interaction. These systems harness multi-view multi-camera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identification (Re-ID), and action understanding (AU). However, deploying CVS in real-world, dynamic environments presents a number of challenges, particularly in addressing occlusions, diverse viewpoints, and environmental variability. Existing surveys have focused primarily on isolated tasks such as tracking, Re-ID, and AU, often neglecting their integration into a cohesive system. These reviews typically emphasize single-view setups, overlooking the complexities and opportunities provided by multi-camera collaboration and multi-view data analysis. To the best of our knowledge, this survey is the first to offer a comprehensive and integrated review of MVMC that unifies MVMC tracking, Re-ID, and AU into a single framework. We propose a unique taxonomy to better understand the critical components of CVS, dividing it into four key parts: MVMC tracking, Re-ID, AU, and combined methods. We systematically arrange and summarize the state-of-the-art datasets, methodologies, results, and evaluation metrics, providing a structured view of the field's progression. Furthermore, we identify and discuss the open research questions and challenges, along with emerging technologies such as lifelong learning, privacy, and federated learning, that need to be addressed for future advancements. The paper concludes by outlining key research directions for enhancing the robustness, efficiency, and adaptability of CVS in complex, real-world applications. We hope this survey will inspire innovative solutions and guide future research toward the next generation of intelligent and adaptive CVS.

Problem

• Addressing multi-camera system challenges like occlusions and viewpoints
• Integrating tracking, re-identification, and action understanding tasks
• Providing comprehensive review of multi-view multi-camera frameworks

Innovation

• Integrated MVMC tracking, Re-ID, and action understanding
• Proposed taxonomy dividing CVS into four key components
• Systematically reviewed datasets, methods, and evaluation metrics