Selection of object detections using overlap map predictions

Advances in deep neural networks have led to significant improvement of object detection accuracy. However, object detection in crowded scenarios is a challenging task for neural networks since extremely overlapped objects provide fewer visible cues for a model to learn from. Further complicating the detection of overlapping objects is the fact that most object detectors produce multiple redundant detections for single objects, which are indistinguishable from detections of separate overlapped objects. Most existing works use some variant of non-maximum suppression to prune duplicate candidate bounding boxes based on their confidence scores and the amount of overlap between predicted bounding boxes. These methods are unaware of how much overlap there actually is between the objects in the image, and are therefore inclined to merge detections for highly overlapped objects. In this paper, we propose an overlap aware box selection solution that uses a predicted overlap map to help it decide which highly overlapping bounding boxes are associated with actual overlapping objects and should not be pruned. We show our solution outperforms the state-of-the-art set-NMS bounding box selection algorithm for both the crowdHuman dataset and a sports dataset.