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.
Abstract (translated)
连接视觉系统（CVS）正在改变从自动驾驶汽车、智慧城市监控到人机交互等多种应用场景。这些系统利用多视点多摄像头(MVMC)数据，通过整合MVMC跟踪、重识别(Re-ID)和行为理解(AU)，提供了增强的情景感知能力。然而，在现实世界的动态环境中部署CVS面临着多种挑战，尤其是处理遮挡、多样视角以及环境变化等问题。现有的调查研究大多集中在诸如追踪、Re-ID和AU等孤立任务上，并往往忽略了将这些技术集成到一个统一系统中的问题。此外，这些审查通常侧重于单视图设置，忽视了多摄像头协作与多视数据分析带来的复杂性和机遇。 据我们所知，本综述是首次提供了一个全面而整合的MVMC评估框架，该框架将MVMC跟踪、Re-ID和AU统一在一个单一的体系结构中。我们提出了一种独特的分类方法来更好地理解CVS的关键组成部分，并将其分为四个主要部分：MVMC跟踪、Re-ID、AU及综合方法。我们系统地整理并总结了当前最先进的数据集、方法论、结果以及评估指标，为该领域的进展提供了一个有条理的视角。 此外，本综述还识别和讨论了开放的研究问题与挑战，并提及了诸如终身学习、隐私保护和联邦学习等新兴技术，在未来的发展中需要解决这些问题。文章最后提出了增强CVS在复杂现实应用中的鲁棒性、效率和适应性的关键研究方向。我们希望这项调查能够激发创新的解决方案，指导未来的研发向着智能且自适应的下一代连接视觉系统迈进。
URL
https://arxiv.org/abs/2510.09731
