2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Las Vegas, NV, United States
June 27, 2016 to June 30, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.260
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.
Proposals, Training, Object detection, Strain, Iterative methods, Computer architecture, Detectors
M. Najibi, M. Rastegari and L. S. Davis, "G-CNN: An Iterative Grid Based Object Detector," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, United States, 2016, pp. 2369-2377.