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In this paper we focus on improving object detection performance in terms ofrecall. We propose a post-detection stage during which we explore the imagewith the objective of recovering missed detections. This exploration isperformed by sampling object proposals in the image. We analyze four differentstrategies to perform this sampling, giving special attention to strategiesthat exploit spatial relations between objects. In addition, we propose a novelmethod to discover higher-order relations between groups of objects.Experiments on the challenging KITTI dataset show that our proposedrelations-based proposal generation strategies can help improving recall at thecost of a relatively low amount of object proposals.

Recovering hard-to-find object instances by sampling context-based object proposals