  Bounding boxes are often used to communicate automatic object detection
results to humans, aiding humans in a multitude of tasks. We investigate the
relationship between bounding box localization errors and human task
performance. We use observer performance studies on a visual multi-object
counting task to measure both human trust and performance with different levels
of bounding box accuracy. The results show that localization errors have no
significant impact on human accuracy or trust in the system. Recall and
precision errors impact both human performance and trust, suggesting that
optimizing algorithms based on the F1 score is more beneficial in
human-computer tasks. Lastly, the paper offers an improvement on bounding boxes
in multi-object counting tasks with center dots, showing improved performance
and better resilience to localization inaccuracy.
