Automatic available seat counting in public rail transport using wavelets

Previously, we introduced an available seat counting algorithm in public rail transport. The main disadvantage of that algorithm is that it lacks automatic event detection. In this paper, we implement two automatic wavelet-based available seat counting algorithms. The new algorithms employ the spatial-domain Laplacian-of-Gaussian-based wavelet, and the frequency-domain Non-Linear Difference of Gaussians-based wavelet bandpass video scene filter for both extracting illumination invariant scene features and, then, combine them efficiently into the background reference frame. Manual segmentation of the scene into rectangles and tiles for detecting an object as seated is no longer needed as we apply now a boundary box tracker on the segmented moving objects' blobs. We test all the algorithms with different video sequences in passengers' train coaches, and compare the previous approach with the two new automatic wavelet-based available seat counting algorithms, and an additional spatial-domain automatic non-wavelet based Simple Mixture of Gaussian Models.