Structured learning via convolutional neural networks for vehicle detection
Ana I. Maqueda, Carlos Roberto Del-Blanco, Fernando Jaureguizar, Narciso N. Garcia · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
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.