Detecting And Measuring Human Walking In Laser Scans

This paper presents work on detecting and tracking human movement in planar range data. Our method stacks multiple planar scans into a 3D frame where time serves as the third dimension. This representation simultaneously informs about the size and shape of the objects in the scene and about their movement, so that no explicit motion models are necessary. The scene is then segmented into 3D spatio-temporal objects which are classified as 'pairs of walking legs' using methods from machine vision. Our main contribution is a novel pre-processing step which aligns the spatio-temporal objects, so that information about the direction and speed of movement is factored out of the representation. The advantage is that the subsequent feature extraction and classification steps are only exposed to movement patterns without reference to direction and speed which are not relevant to recognizing human walking. The method is empirically evaluated and found to significantly increase classification accuracy.