Real time and scene invariant crowd counting: Across a line or inside a region

In this paper, we propose a blob-based method of crowd counting across a line of interest (LOI), which can be further extended to counting inside a region of interest (ROI). Firstly, we detect moving blobs in which low-level features are extracted and grouped. Since features vary with different walking pace, blob velocity is estimated using optical flow, and principal velocity component is further extracted in case of the interference of local articulated motion. Besides, spatial normalization is implemented to compensate for different image depth and different scenes. Finally, we apply Gaussian Process Regression to model the global linear and local nonlinear relationships between the extracted features and crowd counts. The experimental results demonstrate that the proposed method has good applicability to both LOI and ROI crowd counting. As compared with the state-of-the-art methods, our method achieves higher counting accuracy over two representative datasets and meanwhile processes much faster.