Shape improvement of traffic pedestrian hypotheses by means of stereo-vision and superpixels

Shape is a powerful descriptor frequently used in pedestrian detection process. This paper presents a novel stereo and superpixel-based approach for extracting high quality shapes of pedestrian hypotheses from urban traffic scenarios. Gray-levels stereo-vision images of traffic scenes are acquired, high quality stereo-reconstruction and optical flow algorithms are used for computing the depth and motion information. Superpixels are extracted using the intensity images and clustered in different obstacles by a novel paradigm that fuses intensity, depth and motion information. Pedestrian hypotheses are defined as a subset of the scene obstacles obtained by imposing human-specific geometric constraints. A contour tracing algorithm is used for extracting a continuous contour that defines the shape of each pedestrian hypothesis. A comparison between the contours quality of pedestrian hypotheses obtained by this stereo and superpixel approach and another approach based only on stereo-reconstructed points grouping shows improvements in both object shape description and area coverage. Improvements in shape description will increase the accuracy of any further pedestrian detection processes that use pattern matching techniques.