Crowd counting via scale adaptive convolutional neural network in extremely dense crowd images
Crowd counting, a high accuracy and high speed technology, has been applied in new retail, shopping mall, underground, rail station and vehicle surveillance systems. However, due to the inconsistent sizes of human heads, there are a lot of counting errors and instability of the crowd density estimation in extremely dense crowd images. Therefore, a scale adaptive Convolutional Neural Network (CNN) architecture is proposed by introducing residual network on the basis of multi column CNN. In the process of model training, joint learning is proposed in this paper. Through alternating training for residual network and multi column CNN, network parameters with the best accuracy are selected after iteration. Joint learning helps to enhance the modelling ability for massive scale transformation and the scale self adaptability of the network. The proposed method is experimented on public dense crowd data sets. Experimental results prove that scale adaptive CNN shows higher counting capability than the current state of the art method.