Spatial recurrences for pedestrian classification

In this work, a framework is proposed for pedestrian classification based on spatial recurrences in the form of recurrence plots. This representation is more general and potentially more discriminative than a histogram of co-occurrences, since the correlation information between different spatial locations is maintained rather than just summarized into histograms. Recurrences are defined over “states” that encode some visual information at spatial locations over a grid. The framework is general in that it accommodates states of arbitrary nature, any similarity distance between pairs of states, arbitrary grids, and varying degree of recurrence summarization. As revealed experimentally, the resulting descriptor is competitive to recent approaches and compares favourably to an state-of-the-art co-occurrence-based descriptor under several occlusion conditions, and in related problems such as pedestrian view recognition, in particular under stringent quantization conditions. One interesting additional finding is that splitting the recurrent information into multiple recurrence plots turns out to be more discriminative than condensing it into fewer plots