We propose a challenge on the localization and identification of vehicles open to any participant in the world including teams with a track record in the area of machine learning and computer vision. Participants will be invited to test their algorithms on a new dataset prepared and hosted at the University of Sherbrooke and Miovision Inc. Canada. The challenge will be organized around a new traffic dataset which we believe is the largest ever made for that purpose. The dataset consists of more than half a million images acquired at different times of day and different periods of the year by 8,000 traffic cameras deployed all over Canada and the United States. Those images have been selected to cover a wide range of localization challenges and are representative of typical visual data captured today in urban traffic scenarios. Each moving object has been carefully outlined and identified by nearly 200 persons to enable a quantitative comparison and ranking of various algorithms. This dataset aims to provide a rigorous benchmarking facility for training and testing existing and new algorithms for the localization of moving vehicles in traffic scenes.

This website encapsulates a rigorous and comprehensive academic benchmarking effort for testing and ranking existing and new algorithms for vehicle localization and identification in real traffic surveillance scenes. It will maintain a comprehensive ranking of submitted methods for years to come.

Dataset Organizers


The MIO-TCD dataset, original website and utilities associated with this benchmarking facility would not have materialized without the tireless efforts of a lot of people. We would like to recognize the following individuals for their contributions to this effort:

  • Yi Wang, Ph.D student, Université de Sherbrooke, Canada
    Webmaster, software developer

  • Yubin Lin and Chengji Wang (Xiamen University, China)
    Help with ground truthing

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