The 4th AI City Challenge

• URL: http://arxiv.org/abs/2004.14619v1
• Date: Thu, 30 Apr 2020 07:47:14 GMT
• Title: The 4th AI City Challenge
• Authors: Milind Naphade, Shuo Wang, David Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, Rama Chellappa, Pranamesh Chakraborty
• Abstract summary: The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries. The evaluation is conducted on both algorithmic effectiveness and computational efficiency. Results show promise that AI technology can enable smarter and safer transportation systems.
• Score: 80.00140907239279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 leveraged 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 public 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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