Crowd Counting in Images via DSMCNN
Counting the crowd from a single image accurately is always a challenging task. In this work, we propose a Dilated Stacked Multi column Convolutional Neural Network architecture for crowd density estimation in still single images. The model is composed of three columns of convolutional layers with sharing layers. We use smaller kernel and the dilated layer. We stack multifarious pooling layers and optimize the loss function. The DSMCNN model is an end to end and easy trained system. Meanwhile, it shows robust for images with different perspective or crowd density. We demonstrate experiments on the ShanghaiTech dataset and the mall dataset. Introduction Crowd scene analysis has become an important security technique with people pouring into modern cities. Some extremely congested scenes are shown in figure 1.CNN based works [1] [5] in recent years were proposed and have achieved significant improvements in crowd density estimation. But most of these methods suffer drawbacks. Deeper convolution may further shrunk the output size, hard to generate high quality density maps. Moreover, the pooling layer dramatically reduce the spatial resolution so the spatial information of feature map is lost. Meanwhile, the traditional Euclidean loss have some defects [7] like image blur and sensitivity to outliers. When the predicted value differs greatly from the target value, the gradient of the Euclidean Loss may result in gradient explosion. In this paper, we propose a novel Convolutional Neural Network for crowd density estimation and crowd counting aimed at address these problems. Our DSMCNN model could generate a high quality density map and count pedestrians accurately. Moreover, it is extraordinarily convenient to optimize. The summary of our foremost work are as follows: (1) We apply dilated convolution to the network. The dilated convolution can enlarge the receptive field and reduce the spatial resolution rather than increasing parameters or computation amounts. To solve the sensitivity to outliers and image blur caused by traditional Euclidean loss, we add the Manhattan loss to the final loss function. (2) We use a simple but effective variants of pooling module, stacked pooling. We stack the multi kernel pooling layers with multiple receptive fields then concatenating the feature maps together to its successive layer. The larger pooling kernels can provide a wider range of scale invariance, while the smaller pooling kernels can also preserved fine grained information. (3) We propose the Dilated Stacked Multi column Convolutional Neural Network (DSMCNN) architecture. Owing to the smaller kernels, shared parameters and 1 x 1 kernels replacing fully connected layers, our model has less convolutional parameters. There is no need to split the model into three columns and pre train each column independently. See details in 3.3.
DEStech Transactions on Computer Science and Engineering