# The 6th AI City Challenge

arXiv: https://arxiv.org/abs/2204.10380 · HTML: https://mlsanity.com/paper/2204.10380/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 61 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Natural Language Object Retrieval](https://mlsanity.com/paper/1511.04164/) (2015)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Dialog-based Interactive Image Retrieval](https://mlsanity.com/paper/1805.00145/) (2018)
- [Simulating Content Consistent Vehicle Datasets with Attribute Descent](https://mlsanity.com/paper/1912.08855/) (2019)
- [PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data](https://mlsanity.com/paper/2005.00673/) (2020)
- [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021)
- [CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions](https://mlsanity.com/paper/2101.04741/) (2021)
- [The 4th AI City Challenge](https://mlsanity.com/paper/2004.14619/) (2020)
- [Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver](https://mlsanity.com/paper/2204.08096/) (2022)
- [Connecting Language and Vision for Natural Language-Based Vehicle Retrieval](https://mlsanity.com/paper/2105.14897/) (2021)
- [Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond](https://mlsanity.com/paper/2202.14034/) (2022)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval](https://mlsanity.com/paper/2204.08209/) (2022)

## Built on by (most cited first, 23 of 23)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023)
- [A Survey of Federated Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2303.10677/) (2023)
- [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024)
- [HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative](https://mlsanity.com/paper/2403.02640/) (2024)
- [Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities](https://mlsanity.com/paper/2208.14167/) (2022)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer](https://mlsanity.com/paper/2305.08877/) (2023)
- [Training with Product Digital Twins for AutoRetail Checkout](https://mlsanity.com/paper/2308.09708/) (2023)
- [TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios](https://mlsanity.com/paper/2311.18839/) (2023)
- [WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion](https://mlsanity.com/paper/2403.19022/) (2024)
- [Receiver-Centric Generative Semantic Communications](https://mlsanity.com/paper/2411.03127/) (2024)
- [Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout](https://mlsanity.com/paper/2207.05422/) (2022)
- [Ground Plane Projection for Improved Traffic Analytics at Intersections](https://mlsanity.com/paper/2511.12342/) (2025)
- [CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting](https://mlsanity.com/paper/2608.07693/) (2026)
- [FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search](https://mlsanity.com/paper/2608.09474/) (2026)
- [GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction](https://mlsanity.com/paper/2608.09493/) (2026)
- [UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations](https://mlsanity.com/paper/2608.13031/) (2026)
- [VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout](https://mlsanity.com/paper/2204.11024/) (2022)
- [Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback](https://mlsanity.com/paper/2304.04901/) (2023)
- [Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics](https://mlsanity.com/paper/2404.09245/) (2024)
- [TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing](https://mlsanity.com/paper/2507.09448/) (2025)

## Abstract (arXiv)

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.