2022 AI CITY CHALLENGE

AI City means applying AI to improve the efficiency of operations in city environments. This manifests itself in improving transportation outcomes by making traffic more efficient and making roads safer, improving building operations by making them more energy efficient, reducing friction in retail environments by speeding up traffic at retail checkout, etc. The common thread in all these diverse uses of AI is the extraction of actionable insights from a plethora of sensors through real-time streaming and batch analytics of the vast volume and flow of sensor data, such as those from cameras. The AI City Challenge Workshop at CVPR 2022 will specifically focus on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence – The Intelligent Traffic Systems (ITS), and the brick and mortar retail business.

Problems of interest in ITS include:

  • City-scale multi-camera vehicle tracking
  • Natural language-based vehicle track retrieval
  • Naturalistic driver data analytics
  • Anomaly Detection
Problems of interest in retail stores include:

  • Automated checkout
  • Efficient Store utilization

We solicit original contributions in these and related areas where computer vision, natural language processing, and deep learning have shown promise in achieving large-scale practical deployment that will help make our environments smarter and safer.

To accelerate the research and development of techniques, the 6th edition of this Challenge will push the research and development in multiple directions. We will add a brand new track and dataset around naturalistic driving analysis where the data will be captured by several cameras mounted inside the vehicle and focus on driver safety and the task will be to classify driver actions. We will also add a new track evaluating the accuracy of retail store automated checkout using only computer vision sensors. To this end, we will release labeled data for various views of typical retail store goods with the evaluation focused on accurately recognizing and counting the number of such objects at checkout while accounting for clutter, and inter-object visual similarity and occlusion.

Important Dates

ORGANIZING COMMITTEE

Milind Naphade

NVIDIA Corporation

Rama Chellappa

Johns Hopkins University

David Anastasiu

Santa Clara University

Anuj Sharma

Iowa State University

Ming-Ching Chang

University at Albany – SUNY

Stan Sclaroff

Boston University

Shuo Wang

NVIDIA Corporation

Zheng Tang

NVIDIA Corporation

Liang Zheng

Australian National University

Pranamesh Chakraborty

Indian Institute of Technology Kanpur

CITATIONS

Please cite the following papers accordingly if you choose to work with our datasets or refer to the previous challenge results:  

 

2021 challenge summary paper – The 5th AI City Challenge

@InProceedings{Naphade21AIC21,
author = {Milind Naphade and Shuo Wang and David C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Yue Yao and Liang Zheng and Pranamesh Chakraborty and Christian E. Lopez and Anuj Sharma and Qi Feng and Vitaly Ablavsky and Stan Sclaroff},
title = {The 5th AI City Challenge},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
}

2020 challenge summary paper – The 4th AI City Challenge


@InProceedings{Naphade20AIC20,

author = {Milind Naphade and Shuo Wang and David C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Liang Zheng and Anuj Sharma and Rama Chellappa and Pranamesh Chakraborty},

title = {The 4th AI City Challenge},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020},

pages = {2665–2674}

}


2019 challenge summary paper –
 The 2019 AI City Challenge


@InProceedings{Naphade19AIC19,

author = {Milind Naphade and Zheng Tang and Ming-Ching Chang and David C. Anastasiu and Anuj Sharma and Rama Chellappa and Shuo Wang and Pranamesh Chakraborty and Tingting Huang and Jenq-Neng Hwang and Siwei Lyu},
title = {The 2019 AI City Challenge},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019},
pages = {452–460}


2018 challenge summary paper – 
The 2018 AI City Challenge


@inproceedings{Naphade18AIC18,

author={Milind Naphade and Ming-Ching Chang and Anuj Sharma and David C. Anastasiu and Vamsi Jagarlamudi and Pranamesh Chakraborty and Tingting Huang and Shuo Wang and Ming-Yu Liu and Rama Chellappa and Jenq-Neng Hwang and Siwei Lyu},
title = {The 2018 NVIDIA AI City Challenge},
booktitle = {Proc. CVPR Workshops},
pages = {53-–60}, 
year = 2018
}


2017 challenge summary paper – 
The NVIDIA AI City Challenge


@inproceedings{Naphade17AIC17,

author={Milind Naphade and David C. Anastasiu and Anuj Sharma and Vamsi Jagrlamudi and Hyeran Jeon and Kaikai Liu and Ming-Ching Chang and Siwei Lyu and Zeyu Gao},

title={The NVIDIA AI City Challenge},

booktitle = {Prof. SmartWorld},

address = {Santa Clara, CA, USA},

year = 2017

}

 

Natural language-based vehicle retrieval dataset: CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions

@InProceedings{Feng21CityFlowNL,
author={Qi Feng and Vitaly Ablavsky and Stan Sclaroff},
title = {CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions},
howpublished = {arXiv:2101.04741},
year = {2021}

 

Vehicle MTMC tracking & re-identification dataset – CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification


@InProceedings{Tang19CityFlow,

author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield and Shuo Wang and Ratnesh Kumar and David Anastasiu and Jenq-Neng Hwang},

title = {CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019},

pages = {8797–8806}
}


Synthetic 3D vehicle dataset –
 Simulating Content Consistent Vehicle Datasets with Attribute Descent


@InProceedings{Yao20VehicleX,

author={Yue Yao and Liang Zheng and Xiaodong Yang and Milind Naphade and Tom Gedeon},

title = {Simulating Content Consistent Vehicle Datasets with Attribute Descent},
booktitle = {The European Conference on Computer Vision (ECCV)},

month = {August},
year = {2020},

pages = {775–791}