Learning Weight of Losses in Multi-Scale Crowd Counting

In this work, we improve the state-of-the-art in crowd counting by further developing a recently proposed multi-scale, and multi-task crowd counting approach. While most of the studies treat density-based architectures, this study proposed a point-based method for crowd analysis. We propose automatic, and optimal weight assignment to constituents of the loss function. This approach, which is applied to each patch, ensures that the weight parameters are updated in each epoch, and added to the optimizer with model parameters rather than remaining constant. For validation of our proposed approach, we use three popular crowd counting datasets, ShanghaiTech A, ShanghaiTech B, and UCF_CC_50. The performance of our approach exceeds the performances of the other studies on the ShanghaiTech dataset, and is highly competitive with the performances of the other studies on the UCF CC 50 dataset.