MSCDP: Multi-Step Crowd Density Predictor in Indoor Environment

Abstract Monitoring and predicting crowd movements in indoor environments are of great importance in crowd management to prevent crushing and trampling. Existing works mostly focused on individual trajectory forecasting in a less crowded scene, or crowd counting and density estimation. Only a very few works predict the crowd density distribution. However, this study is failing to realize multi-step prediction or exploiting only density heatmaps modality ignores the information complementation with corresponding video frames. Therefore, we are motivated to predict crowd density distribution in multiple time steps to facilitate long-term prediction. In this paper, a Multi-Step Crowd Density Predictor (MSCDP) to fuse video frame sequences and corresponding density heatmaps, is proposed to accurately forecast the future crowd density heatmaps. To capture long-term periodic movement features, the long-term optical flow context memory (LOFCM) module is designed to store learnable patterns. We conducted extensive experiments on two real-world datasets. Evaluation results show that our MSCDP outperforms the state-of-the-art baseline techniques and MSCDP variants in terms of various prediction errors, demonstrating the effectiveness of MSCDP and each of its key components in multi-step crowd density prediction.