# Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey arXiv: https://arxiv.org/abs/2510.09731 ยท HTML: https://mlsanity.com/paper/2510.09731/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 103 of its 251 references that are in the map. - First posted on arXiv 10 Oct 2025. ## Builds on (most cited first, 30 of 103) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016) - [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020) - [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019) - [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020) - [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017) - [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018) - [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019) - [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022) - [Human Action Recognition from Various Data Modalities: A Review](https://mlsanity.com/paper/2012.11866/) (2020) - [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019) - [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021) - [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017) - [The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose](https://mlsanity.com/paper/2007.00394/) (2020) - [Unsupervised Pre-training for Person Re-identification](https://mlsanity.com/paper/2012.03753/) (2020) - [MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries](https://mlsanity.com/paper/2205.00613/) (2022) - [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018) - [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022) - [Multiview Detection with Feature Perspective Transformation](https://mlsanity.com/paper/2007.07247/) (2020) - [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019) - [Person Re-identification Method Based on Color Attack and Joint Defence](https://mlsanity.com/paper/2111.09571/) (2021) - [Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.04337/) (2021) - [Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection](https://mlsanity.com/paper/1704.05775/) (2017) - [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023) - [Deep Multi-camera People Detection](https://mlsanity.com/paper/1702.04593/) (2017) - [Video-based Person Re-identification with Spatial and Temporal Memory Networks](https://mlsanity.com/paper/2108.09039/) (2021) - [Part-Aware Transformer for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.03322/) (2023) - [LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking](https://mlsanity.com/paper/2111.11892/) (2021) - [Region-based Quality Estimation Network for Large-scale Person Re-identification](https://mlsanity.com/paper/1711.08766/) (2017) - [Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking](https://mlsanity.com/paper/2203.14208/) (2022) ## Built on by (most cited first, 1 of 1) - [Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving](https://mlsanity.com/paper/2603.06544/) (2026) ## Abstract (arXiv) 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.