Learning-Deep-Learning

Probabilistic Object Detection: Definition and Evaluation

December 2019

tl;dr: Proposes a new metric for probabilistic detection.

Overall impression

Proposed a benchmark PDQ (probabilistic detection quality) to evaluate probabilistic object detectors.

\[PDQ = \sqrt{DQ * LQ}\]

\(DQ = \exp(-(L_{FG} + L_{BG}))\) \(L_{FG} = -\frac{1}{|GT|}\sum \log p(TP)\) \(L_{BG} = -\frac{1}{|GT|}\sum \log (1 - p(FP))\) \(LQ = p(class=y)\)

Key ideas

• Only pixels in the original mask is counted as TP. Only pixels not in the original bbox is counted as FP.
• For bbox annotation, we can use the bbox as the mask.

Technical details

• Summary of technical details

Notes

• A Mask-RCNN Baseline for Probabilistic Object Detection provides a benchmark with mask rcnn. The authors change the output of mask rcnn to probabilistic approach by
  • shrinking bbox by 10%
  • set uncertainty to 20% of width/height