# Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment arXiv: https://arxiv.org/abs/2304.09471 ยท HTML: https://mlsanity.com/paper/2304.09471/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 58 references that are in the map. - First posted on arXiv 19 Apr 2023. ## Builds on (most cited first, 30 of 34) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016) - [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021) - [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017) - [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020) - [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020) - [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019) - [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022) - [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022) - [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019) - [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018) - [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017) - [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019) - [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022) - [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019) - [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021) - [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018) - [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016) - [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://mlsanity.com/paper/1910.06827/) (2019) - [Batch DropBlock Network for Person Re-identification and Beyond](https://mlsanity.com/paper/1811.07130/) (2018) - [Segment as Points for Efficient Online Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2007.01550/) (2020) - [LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking](https://mlsanity.com/paper/2111.11892/) (2021) - [MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark](https://mlsanity.com/paper/2111.15157/) (2021) - [The 6th AI City Challenge](https://mlsanity.com/paper/2204.10380/) (2022) - [Training Deep Face Recognition Systems with Synthetic Data](https://mlsanity.com/paper/1802.05891/) (2018) - [Deep Domain Adaptive Object Detection: a Survey](https://mlsanity.com/paper/2002.06797/) (2020) - [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023) - [Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques](https://mlsanity.com/paper/2005.01385/) (2020) ## Built on by (most cited first, 9 of 9) - [Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2306.13074/) (2023) - [Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking](https://mlsanity.com/paper/2311.03561/) (2023) - [Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation](https://mlsanity.com/paper/2509.09946/) (2025) - [Enhancing Multi-Camera Gymnast Tracking Through Domain Knowledge Integration](https://mlsanity.com/paper/2511.16532/) (2025) - [ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment](https://mlsanity.com/paper/2409.00349/) (2024) - [A Robust Anchor-based Method for Multi-Camera Pedestrian Localization](https://mlsanity.com/paper/2410.21308/) (2024) - [Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking](https://mlsanity.com/paper/2505.18111/) (2025) - [Glance-MCMT: A General MCMT Framework with Glance Initialization and Progressive Association](https://mlsanity.com/paper/2507.10115/) (2025) - [Understanding Identity Continuity in Thermal Video through Scene-Level Consistency](https://mlsanity.com/paper/2606.01694/) (2026) ## Abstract (arXiv) Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI.