The 4th AI City Challenge

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The 4th AI City Challenge

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

Abstract

The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries, who leverage city-scale real traffic data and high-quality synthetic data to compete in four challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation is conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. The evaluation system shows two leader boards, in which a general leader board shows all submitted results, and a public leader board shows results limited to our contest participation rules, that teams are not allowed to use external data in their work. The general leader board shows results more close to real-world situations where annotated data are limited. Our results show promise that AI technology can enable smarter and safer transportation systems.

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Dokumentinformationen

Titel :

The 4th AI City Challenge

Beteiligte:

Naphade, Milind (Autor:in) / Wang, Shuo (Autor:in) / Anastasiu, David C. (Autor:in) / Tang, Zheng (Autor:in) / Chang, Ming-Ching (Autor:in) / Yang, Xiaodong (Autor:in) / Zheng, Liang (Autor:in) / Sharma, Anuj (Autor:in) / Chellappa, Rama (Autor:in) / Chakraborty, Pranamesh (Autor:in)

Erschienen in:

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ; 2665-2674

Erscheinungsdatum :

2020-06-01

Format / Umfang :

1632250 byte

ISBN :

978-1-7281-9360-1

ISSN :

2160-7516

DOI :

https://doi.org/10.1109/CVPRW50498.2020.00321