Structured learning via convolutional neural networks for vehicle detection

Ana I. Maqueda; Carlos R. del Blanco; Fernando Jaureguizar; Narciso García

doi:10.1117/12.2261982

1 May 2017 Structured learning via convolutional neural networks for vehicle detection

Ana I. Maqueda, Carlos R. del Blanco, Fernando Jaureguizar, Narciso García

Proceedings Volume 10223, Real-Time Image and Video Processing 2017; 1022302 (2017) https://doi.org/10.1117/12.2261982
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.

Citation Download Citation

Ana I. Maqueda, Carlos R. del Blanco, Fernando Jaureguizar, and Narciso García "Structured learning via convolutional neural networks for vehicle detection", Proc. SPIE 10223, Real-Time Image and Video Processing 2017, 1022302 (1 May 2017); https://doi.org/10.1117/12.2261982

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