Izvorni znanstveni članak
https://doi.org/10.7305/automatika.53-4.227
Automatic Passengers Counting In Public Rail Transport Using Wavelets
Pieterjan De Potter
; Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, B-9050 Ledeberg-Ghent, Belgium
Ioannis Kypraios
; Head of APEM Computing Lab, Centre for Innovation & Enterprise, Begbroke Science Park, University of Oxford, Begbroke Hill,Woodstock Road, Begbroke, Oxfordshire, OX5 1PF
Steven Verstockt
; Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, B-9050 Ledeberg-Ghent, Belgium
Chris Poppe
; Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, B-9050 Ledeberg-Ghent, Belgium
Rik Van de Walle
; Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, B-9050 Ledeberg-Ghent, Belgium
Sažetak
Previously, we introduced a passengers’ counting algorithm in public rail transport. The main disadvantage of that algorithm is it lacks automatic event detection. In this article, we implement two automatic wavelet-based passengers counting algorithms. The new algorithms employ the spatial-domain Laplacian-of-Gaussian-based wavelet, and the frequency-domain applied Non-Linear Difference of Gaussians-based wavelet bandpass video scene filters to extract illumination invariant scene features and to 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 now apply a boundary box tracker on the segmented moving objects’ blobs. A scene map is combined with the wavelet-based methods and the boundary box for multi-camera object registration. We have developed a novel holistic geometrical approach for exploiting the scene map and the recorded video sequences from both cameras installed in each train coach to separate the detected objects and locate their positions on the scene map. We test all the algorithms with several video sequences recorded from the both cameras installed in each train coach. We compare the previously developed non-automatic passengers’ counting algorithm with the two new automatic wavelet-based passengers’ counting algorithms, and an additional spatial-domain automatic non-wavelet based Simple Mixture of Gaussian Models algorithm.
Ključne riječi
Video analytics; Event detection; Automatic passengers’ seats counting; Wavelets; Laplacian-of-Gaussian; Non-Linear Difference of Gaussians; Simple Mixture of Gaussians; Illumination invariant; Frequency and spatial domain
Hrčak ID:
93925
URI
Datum izdavanja:
5.12.2012.
Posjeta: 2.120 *