People Detection and Pose Classification Inside a Moving Train Using Computer Vision

This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017) Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10645) The use of surveillance video cameras in public transport is increasingly regarded as a solution to control vandalism and emergency situations. The widespread use of cameras brings in the problem of managing high volumes of data, resulting in pressure on people and resources. We illustrate a possible step to automate the monitoring task in the context of a moving train (where popular background removal algorithms will struggle with rapidly changing illumination). We looked at the detection of people in three possible postures: Sat down (on a train seat), Standing and Sitting (half way between sat down and standing). We then use the popular Histogram of Oriented Gradients (HOG) descriptor to train Support Vector Machines to detect people in any of the predefined postures. As a case study, we use the public BOSS dataset. We show different ways of training and combining the classifiers obtaining a sensitivity performance improvement of about 12% when using a combination of three SVM classifiers instead of a global (all classes) classifier, at the expense of an increase of 6% in false positive rate. We believe this is the first set of public results on people detection using the BOSS dataset so that future researchers can use our results as a baseline to improve upon. The work described here was carried out as part of the OBSERVE project funded by the Fondecyt Regular Program of Conicyt (Chilean Research Council for Science and Technology) under grant no. 1140209. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander.