A layered-based fusion-based approach to detect and track the movements of pedestrians through partially occluded situations

To obtain perception abilities, conventional methods independently detect static and dynamic obstacles, and estimate their related information, which is not quite reliable and computationally heavy. We propose a fusion-based and layered-based approach to systematically detect dynamic obstacles and obtain their location and timing information. The layered-based concept helps us to first search pedestrians in horizontal dimension based on transitional peaks in the defined projection-curves, and then search in vertical dimension. Converting a typical 2D search problem into two 1D search problems significantly decreases the computational load. The fusion-based obstacle detection fuses the information from initial segmentation and dynamic tracking model to avoid complicated tracking schemes. The methodologies take advantage of connection between different information, and increase the accuracy and reliability of obstacle segmentation and tracking. The search mechanism works for both visible and infrared sequences, and is specifically effective to track the movements of pedestrians in complicated environments such as human intersecting and conclusion, thus improving environment understanding abilities and driving safety.