Stereo- and neural network-based pedestrian detection

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate.

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