Part based pedestrian detection based on Logic inference
Daniel Olmeda, José María Armingol, Arturo de la Escalera · 2013
This paper presents an approach on detection of largely occluded pedestrians. From a pair of synchronized cameras in the Visible Light (VL) and Far Infrared (FIR) spectrum individual detections are combined and final confidence is inferred using a small set of logic rules via a Markov Logic Network. Pedestrians not entirely contained in the image or occluded are detected based on the binary classification on subparts of the detection window. The presented method is applied to a pedestrian classification problem in urban environments. The classifier has been tested in an Intelligent Transportation System (ITS) platform as part of an Advanced Driver Assistance Systems (ADAS).