A Vehicle Type Recognition Method based on Sparse Auto Encoder
- 10.2991/cisia-15.2015.88How to use a DOI?
- vehicle type recognition; sparse AutoEncoder; multi-level features fusing
In recent years, feature learning methods based on unsupervised learning get more and more attention. Until now, Unsupervised feature learning has been applied to solve many problems such as detection, recognition and classification. In this paper, we propose a deep feature learning method based on Sparse AutoEncoder to recognize vehicle types and to improve the classification accuracy rate. First we used Sparse AutoEncoder to generate the convolutional kernel and used the convolutional kernel to generate convolutional feature. Then pooling was applied. We repeated the network several times to construct a deep feature learning framework. To improve performance, we also combined the feature learned in different layer to form a new feature vector and applied PCA to reduce the dimension. Finally we used softmax to recognize the vehicle type. Adopting the local receive field, we can reduce the parameters. The experiment shows that our method can improve the performance a little.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - H.L Rong AU - Y.X Xia PY - 2015/06 DA - 2015/06 TI - A Vehicle Type Recognition Method based on Sparse Auto Encoder BT - Proceedings of the International Conference on Computer Information Systems and Industrial Applications PB - Atlantis Press SP - 323 EP - 326 SN - 2352-538X UR - https://doi.org/10.2991/cisia-15.2015.88 DO - 10.2991/cisia-15.2015.88 ID - Rong2015/06 ER -