Estimation of crowd density in surveillance scenes based on deep convolutional neural network

As an effective way for crowd monitoring, control and behavior understanding, crowd density estimation is an important research topic in artificial intelligence applications. In this paper, we propose a new crowd density estimation method by deep convolutional neural network (ConvNet). The contributions are two-folds: first, typical deep networks are imported for crowd density estimation. Second, a new dataset including 31 crowd Subway-carriage scenes with over 160K density annotated images is introduced to better evaluate the accuracy of cross-scene crowd density estimation methods. Experiment results confirm the good performance of our proposed method for real-world application.