Pedestrian Counting via Deep Convolutional Neural Networks In Crowded Scene
Jingwei Li, Jianxin Song
Available Online September 2016.
- https://doi.org/10.2991/amitp-16.2016.67How to use a DOI?
- pedestrian, counting, crowd, CNN, EIN.
- Currently pedestrian counts mainly faces two major problems in the crowd scene: expression of pedestrian's features and perspective shade. To address this problem, we propose a deep convolutional neural network (CNN) and Ensemble Inference Network (EIN) for crowd pedestrian recognizing and counting. First, we propose a Perspective-Correct Interpolation model for extract more features in crowded scenes. Then, we design a convolution layer which contains preventing occlusion layer in the neural network. Finally to achieve lower error rates, our CNN-based method introduces Ensemble Inference Network to the training and classification processes. Experimental results show that the error rate of the proposed method is 0.28 and possess accurate recognition in pedestrian comparable performance to state-of-the-art methods in crowded scenes.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jingwei Li AU - Jianxin Song PY - 2016/09 DA - 2016/09 TI - Pedestrian Counting via Deep Convolutional Neural Networks In Crowded Scene BT - Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016) PB - Atlantis Press SP - 341 EP - 347 SN - 2352-538X UR - https://doi.org/10.2991/amitp-16.2016.67 DO - https://doi.org/10.2991/amitp-16.2016.67 ID - Li2016/09 ER -