Self-Bootstrapping Pedestrian Detection in Downward-Viewing Fisheye Cameras Using Pseudo-Labeling

Downward-viewing fisheye cameras have attracted much attention in surveillance systems due to the wide coverage and less occlusion. However, pedestrian detection in downward-viewing fisheye cameras remains an open problem due to a lack of large-scale labeled dataset as existing datasets are usually based on oblique-viewing perspective cameras. Furthermore, it's time-consuming to label a downward-viewing fisheye dataset manually. To address this, we propose a self-bootstrapping pedestrian detection method, which automatically pseudo-labels downward-viewing fisheye images by making full use of spatial and temporal consistency of pedestrians in the cameras to promote the accuracy of pedestrian detection. We segment the downward-viewing fisheye images into two regions and propose the pseudo-labeling methods for them progressively: a cyclic fine-tuned detector for the oblique region and a visual tracking method for the vertical region. Combining the pseudo-labels from two regions, we fine-tune our detection network for better accuracy. Experimental results show that the proposed approach reduces time consumption by about 95% compared with labor-intensive manual labeling while it still reaches competitive and comparable Average Precision (AP).