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, Portekiz, 27 - 29 Ocak 2018, ss.137-144 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.5220/0006533601370144
  • Basıldığı Şehir: Funchal
  • Basıldığı Ülke: Portekiz
  • Sayfa Sayıları: ss.137-144


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.