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In traffic engineering, vehicle detectors are trained on limited datasetsresulting in poor accuracy when deployed in real world applications. Annotatinglarge-scale high quality datasets is challenging. Typically, these datasetshave limited diversity; they do not reflect the real-world operatingenvironment. There is a need for a large-scale, cloud based positive andnegative mining (PNM) process and a large-scale learning and evaluation systemfor the application of traffic event detection. The proposed positive andnegative mining process addresses the quality of crowd sourced ground truthdata through machine learning review and human feedback mechanisms. Theproposed learning and evaluation system uses a distributed cloud computingframework to handle data-scaling issues associated with large numbers ofsamples and a high-dimensional feature space. The system is trained usingAdaBoost on $1,000,000$ Haar-like features extracted from $70,000$ annotatedvideo frames. The trained real-time vehicle detector achieves an accuracy of atleast $95\%$ for $1/2$ and about $78\%$ for $19/20$ of the time when tested onapproximately $7,500,000$ video frames. At the end of 2015, the dataset isexpect to have over one billion annotated video frames.

Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection