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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL and ImageNet, and impact on DPM and R-CNN detection
performance. Our analysis shows that for object detection improving proposal
localisation accuracy is as important as improving recall. We introduce a novel
metric, the average recall (AR), which rewards both high recall and good
localisation and correlates surprisingly well with detector performance. Our
findings show common strengths and weaknesses of existing methods, and provide
insights and metrics for selecting and tuning proposal methods.
