An extension of BING to high IOU threshold

Abstract

BING is an objectness measure to extract proposal windows in an image that may contain objects, avoiding cumbersome sliding window search for object detection. BING has a high recall rate when the Intersection-over-Union (IOU) threshold is 0.5, and runs as fast as 300 fps. However, the recall rate drops rapidly when the IOU threshold is greater than 0.5. So in this paper, we focus on investigating the cause of this phenomenon, and propose how to improve the recall rates, in which average recall rate is used in the performance evaluation of objectness measure for object detection. The problem of less positive samples in the secondary training stage is solved by selecting parameters with respect to training and testing.