# City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

arXiv: https://arxiv.org/abs/2105.06623 · HTML: https://mlsanity.com/paper/2105.06623/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 23 of 23)

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [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)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Multi-Domain Learning and Identity Mining for Vehicle Re-Identification](https://mlsanity.com/paper/2004.10547/) (2020)
- [Unity Style Transfer for Person Re-Identification](https://mlsanity.com/paper/2003.02068/) (2020)
- [1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking](https://mlsanity.com/paper/2101.08040/) (2021)
- [Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification](https://mlsanity.com/paper/1901.06129/) (2019)
- [Multi-Object Tracking with Siamese Track-RCNN](https://mlsanity.com/paper/2004.07786/) (2020)
- [Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model](https://mlsanity.com/paper/2008.09785/) (2020)

## Built on by (most cited first, 18 of 18)

- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures](https://mlsanity.com/paper/2205.04713/) (2022)
- [Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information](https://mlsanity.com/paper/2301.07805/) (2023)
- [Real-time Lane-wise Traffic Monitoring in Optimal ROIs](https://mlsanity.com/paper/2404.15212/) (2024)
- [VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network](https://mlsanity.com/paper/2312.06428/) (2023)
- [RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking](https://mlsanity.com/paper/2507.08729/) (2025)
- [Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking](https://mlsanity.com/paper/2211.15538/) (2022)
- [Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach](https://mlsanity.com/paper/2304.00950/) (2023)
- [CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching](https://mlsanity.com/paper/2307.02753/) (2023)
- [City-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model](https://mlsanity.com/paper/2405.11345/) (2024)
- [A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic](https://mlsanity.com/paper/2505.00534/) (2025)
- [Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking](https://mlsanity.com/paper/2204.07442/) (2022)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking](https://mlsanity.com/paper/2410.02638/) (2024)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking](https://mlsanity.com/paper/2209.09606/) (2022)
- [TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing](https://mlsanity.com/paper/2507.09448/) (2025)
- [Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment](https://mlsanity.com/paper/2511.13904/) (2025)

## Abstract (arXiv)

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDETracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard. The code have released: https://github.com/LCFractal/AIC21-MTMC.