G-CNN: An Iterative Grid Based Object Detector

Mahyar Najibi; Mohammad Rastegari; Larry S. Davis

CVPR 2016spotlight267 citations

G-CNN: An Iterative Grid Based Object Detector

Mahyar Najibi, Mohammad Rastegari, Larry S. Davis

Abstract

We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.

BibTeX

@inproceedings{cvpr2016_gcnnaniterativeg,
  title = {G-CNN: An Iterative Grid Based Object Detector},
  author = {Mahyar Najibi and Mohammad Rastegari and Larry S. Davis},
  booktitle = {CVPR 2016},
  year = {2016}
}