Crowd Density Forecasting with Distributed Camera-Based Nodes using Encoder-Decoder Long Short-Term Memory Network

Integration of multiple camera-based devices, devices designed for real-time crowd counting, is explored for crowd density forecasting in an area. Crowd density forecasting in such case, where future crowd counts in multiple time steps for each sensing region of individual cameras are being predicted, is a peculiar task. This paper employs Encoder-Decoder Long Short-Term Memory network to solve this problem. Overcounting may also exist in the presence of overlapping sensing regions of the cameras which is tackled in this work by correcting the crowd counts before forecasting through a neural network-based approach. Thus, this study proposes a two-stream network for crowd density forecasting. The network is trained and evaluated on data generated from an actual setup emulating the presumed output of multiple camera-based crowd-counting devices. Overall, the proposed framework delivers promising results based on the evaluation.