Concealed Objects in a Scene: A Closer Look at the Localization Block of Object Detection Models

Object detection models typically accomplish two tasks, the localization and classification of objects. Object localization algorithms have come a long way in the advancement of computer vision applications. However, object detection models still face challenges in localizing objects that are concealed in a scene. In this paper, we investigate the localization block of different object detectors when detecting these types of objects. Our results show that for localizing objects with high color and texture similarity with its immediate background, region proposal networks perform better than single-stage networks. To relate such blending characteristics of objects with its environments, as one possible cause of misdetection, we examine the characteristics of commonly misdetected objects with high color and texture similarity with its background. Statistical study reveals that commonly misdetected objects have significantly similar color and texture features than those of the detected objects. This implies that color and texture similarity relate to misdetection in object detection. To the best of our knowledge, this work presents the first empirical study on localizing objects with high color and similarity with its background.