Efficient detection of crossing pedestrians from a moving vehicle with an array of cameras

We describe a method for detecting crossing pedestrians and, in general, any object that is moving perpendicular to the driving direction of the vehicle. This is achieved by combining video snapshots from multiple cameras that are placed in a linear configuration and from multiple time instances. We demonstrate that the proposed array configuration imposes tight constraints on the expected disparity of static objects in a certain image region for a given camera pair. These regions are distinct for different camera pairs. In that manner, static regions can generally be distinguished from moving targets throughout the entire field of view when analyzing enough pairs, requiring only straightforward image processing techniques. On a self-captured dataset with crossing pedestrians, our proposed method reaches an F1 detection score of 83.66% and a mean average precision (MAP) of 84.79% on an overlap test when used stand-alone, being processed at 59 frames per second without GPU acceleration. When combining it with the Yolo V4 object detector in cooperative fusion, the proposed method boosts the maximal F1 scores of this detector on this same dataset from 87.86% to 92.68% and the MAP from 90.85% to 94.30%. Furthermore, combining it with the lower power Yolo-Tiny V4 detector in the same way yields F1 and MAP increases from 68.57% to 81.16% and 72.32% to 85.25%, respectively