  Object proposal has become a popular paradigm to replace exhaustive sliding
window search in current top-performing methods in PASCAL VOC and ImageNet.
Recently, Hosang et al. conduct the first unified study of existing methods' in
terms of various image-level degradations. On the other hand, the vital
question "what object-level characteristics really affect existing methods'
performance?" is not yet answered. Inspired by Hoiem et al.'s work in
categorical object detection, this paper conducts the first meta-analysis of
various object-level characteristics' impact on state-of-the-art object
proposal methods. Specifically, we examine the effects of object size, aspect
ratio, iconic view, color contrast, shape regularity and texture. We also
analyse existing methods' localization accuracy and latency for various PASCAL
VOC object classes. Our study reveals the limitations of existing methods in
terms of non-iconic view, small object size, low color contrast, shape
regularity etc. Based on our observations, lessons are also learned and shared
with respect to the selection of existing object proposal technologies as well
as the design of the future ones.
