For more details please refer to There is an update on the track 1 dataset. Team 97 ANU: Track1 Track2. Good habits start young! Unfortunately, progress has been limited for several reasons — among them, poor data quality, missing data labels, and the lack of high-quality models that can convert the data into actionable insights There is also a need for platforms that can handle analysis from the edge to the cloud, which will accelerate the development and deployment of these models. For more details please refer to There is an update on the track 3 dataset. The teams participating in the 2019 AI City Challenge will be among the first researchers working on this benchmark, and we believe your work will create a significant impact in cross-camera vehicle tracking and re-identification. AICity_Team6_ISU Source code and code description of Team6_ISU for NVIDIA AICity Challenge 2017 track 1 The code from the top teams in the 2019 AI City Challenge (not in any paraticular order) Team 12 BUPT: Track1 & Track2 Track3. Youth City Challenge Race: Bayonne PHOTOS. Review the terms and conditions from 2019 . The code from the top teams in the 2019 AI City Challenge 13 93 2 0 Updated May 11, 2020. Please follow the Presentation of papers and announcement of awards: June 16Announcement of Challenge Winners and Awards CeremonyOur paper based on the benchmarks of Track 1 and Track 2 of the 2019 AI City Challenge has been accepted for oral presentation at the CVPR 2019 main conference. 2018AICITY_Maryland Python 7 20 4 0 Updated Aug 16, 2019.

Team 113 UCMUS: Track2 Track3. Team 21 UWIPL: Track1 Track2 Track3 CITY CHALLENGE RACE STADIUM - NEW ENGLAND. Team 49 DiDi Global DDashcam: Track1. Top students from the initial challenge course will be selected for one of 300 follow-up scholarships to either the Deep Learning or Computer Vision Nanodegree programs.

The second change in this edition will be the introduction of augmented synthetic data for the purpose of substantially increasing the training set for the task of re-identification.Presentation of papers and announcement of awards: Monday, June 15All accepted workshop papers are finalized. For more details please refer to We have added a supplementary file to the Track 1 package. Results: 2019 Hoboken. Upcoming Races. Team 59 Baidu Zero_One: Track1 & Track2. For more details please refer to We have updated the evaluation kit in the Track 3 package. Immense opportunity exists to make transportation systems smarter, based on sensor data from traffic, signaling systems, infrastructure, and transit. It will focus on Intelligent Transportation System (ITS) problems, such as:Traffic anomaly detection – Leveraging unsupervised learning to detect anomalies such as lane violation, illegal U-turns, wrong-direction driving, etc.We solicit original contributions in these and related areas where computer vision and specifically deep learning have shown promise in achieving large-scale practical deployment that will help make cities smarter.To accelerate the research and development of techniques that rely less on supervised approaches and more on transfer learning, self-supervised and semi-supervised learning we are organizing this Challenge. For more details please refer to The evaluation server is now open for submission. Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019 Yucheng Chen1, Longlong Jing2, Elahe Vahdani2, Ling Zhang3, Mingyi He1, and Yingli Tian2,3∗ 1Northwestern Polytechnical University, Xi’an, China, 710129 2The Graduate Center, The City University of New York, NY, 10016 3The City College, The City University of New York, NY 10031 The datasets for the 2019 AI City Challenge, i.e., CityFlow (for MTMC vehicle tracking and re-identification) and the Iowa DOT Traffic Dataset (for traffic anomaly detection), can be accessed now.The evaluation server is open again for submission of test results. Please note that the official name of our benchmark for Track 1 is “CityFlow,” and the benchmark of Track 2 is a subset of that, named “CityFlow-ReID.” The paper is available author = {Tang, Zheng and Naphade, Milind and Liu, Ming-Yu and Yang, Xiaodong and Birchfield, Stan and Wang, Shuo and Kumar, Ratnesh and Anastasiu, David and Hwang, Jenq-Neng},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)},author = {Naphade, Milind and Tang, Zheng and Chang, Ming-Ching and Anastasiu, David C. and Sharma, Anuj and Chellappa, Rama and Wang, Shuo and Chakraborty, Pranamesh and Huang, Tingting and Hwang, Jenq-Neng and Lyu, Siwei},booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

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