Large-scale machine learning and evaluation platform for real-time traffic surveillance

Justin A. Eichel; Akshaya Mishra; Nicholas Miller; Nicholas Jankovic; Mohan A. Thomas; Tyler Abbott; Douglas Swanson; Joel Keller

doi:10.1117/1.JEI.25.5.051204

23 March 2016 Large-scale machine learning and evaluation platform for real-time traffic surveillance

Justin A. Eichel, Akshaya Mishra, Nicholas Miller, Nicholas Jankovic, Mohan A. Thomas, Tyler Abbott, Douglas Swanson, Joel Keller

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 051204 (March 2016). https://doi.org/10.1117/1.JEI.25.5.051204

Abstract

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ∼7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

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Justin A. Eichel, Akshaya Mishra, Nicholas Miller, Nicholas Jankovic, Mohan A. Thomas, Tyler Abbott, Douglas Swanson, and Joel Keller "Large-scale machine learning and evaluation platform for real-time traffic surveillance," Journal of Electronic Imaging 25(5), 051204 (23 March 2016). https://doi.org/10.1117/1.JEI.25.5.051204

Published: 23 March 2016

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