CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification

Lakhal M. I., Escalera S., ÇEVİKALP H.

13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / International Conference on Computer Vision Theory and Applications (VISAPP), Funchal, Portugal, 27 - 29 January 2018, pp.137-144, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.5220/0006533601370144
  • City: Funchal
  • Country: Portugal
  • Page Numbers: pp.137-144
  • Keywords: Vehicle Classification, Deep Learning, End-to-end Learning, REPRESENTATION, SYSTEM
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset.