Confluence is a novel non-Intersection over Union (IoU) alternative to
Non-Maxima Suppression (NMS) in bounding box post-processing in object
detection. It overcomes the inherent limitations of IoU-based NMS variants to
provide a more stable, consistent predictor of bounding box clustering by using
a normalized Manhattan Distance inspired proximity metric to represent bounding
box clustering. Unlike Greedy and Soft NMS, it does not rely solely on
classification confidence scores to select optimal bounding boxes, instead
selecting the box which is closest to every other box within a given cluster
and removing highly confluent neighboring boxes. Confluence is experimentally
validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision
by up to 2.3-3.8% and Average Recall by up to 5.3-7.2% when compared against
de-facto standard and state of the art NMS variants. Quantitative results are
supported by extensive qualitative analysis and threshold sensitivity analysis
experiments support the conclusion that Confluence is more robust than NMS
variants. Confluence represents a paradigm shift in bounding box processing,
with potential to replace IoU in bounding box regression processes.Comment: 13 page