Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning

In this paper, we tackle the problem of crowd counting in indoor videos, where people often stay almost static for a long time. The label distribution, which covers a certain number of crowd counting labels, representing the degree to which each label describes the video frame, is previously adopted to model the label ambiguity of the crowd number. However, since the label ambiguity is significantly affected by the crowd number of the scene, we initialize the label distribution of each frame by the discretized Gaussian distribution with adaptive variance instead of the original single static Gaussian distribution. Moreover, considering the gradual change of crowd numbers in the adjacent frames, a mixture of Gaussian models is proposed to generate the final label distribution representation for each frame. The weights of the Gaussian models rely on the frame and feature distances between the current frame and the adjacent frames. The mixed $\ell _{2,1}$ -norm is adopted to restrict the weights of predicting the adjacent crowd numbers to be locally correlated. We collect three new indoor video datasets with frame number annotation for further research. The proposed approach achieves state-of-the-art performance on seven challenging indoor videos and cross-scene experiments.