G-CNN: An Iterative Grid Based Object Detector

Mahyar Najibi, Mohammad Rastegari, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2369-2377

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Najibi_2016_CVPR,
author = {Najibi, Mahyar and Rastegari, Mohammad and Davis, Larry S.},
title = {G-CNN: An Iterative Grid Based Object Detector},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}