FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
This improves object detection accuracy in scenes with high object overlap, but it is an incremental enhancement to existing NMS methods.
The paper tackles the problem of duplicate detections in object detection by proposing FeatureNMS, which uses feature embeddings to recognize duplicates beyond bounding box overlap, achieving state-of-the-art performance.
Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: This heuristic assumes that a high overlap between two bounding boxes corresponds to a high probability of one being a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.