Recovering hard-to-find object instances by sampling context-based object proposals
This work addresses recall improvement in object detection for applications like autonomous driving, but it is incremental as it builds on existing proposal methods.
The paper tackles the problem of low recall in object detection by introducing a post-detection stage that samples object proposals to recover missed detections, with experiments on the KITTI dataset showing improved recall at a low proposal cost.
In this paper we focus on improving object detection performance in terms of recall. We propose a post-detection stage during which we explore the image with the objective of recovering missed detections. This exploration is performed by sampling object proposals in the image. We analyze four different strategies to perform this sampling, giving special attention to strategies that exploit spatial relations between objects. In addition, we propose a novel method to discover higher-order relations between groups of objects. Experiments on the challenging KITTI dataset show that our proposed relations-based proposal generation strategies can help improving recall at the cost of a relatively low amount of object proposals.