Scalable, high-quality object detection
Venue
arXiv (2015)
Publication Year
2015
Authors
Christian Szegedy, Scott Reed, Dumitru Erhan, Dragomir Anguelov
BibTeX
Abstract
Most high quality object detection approaches use the same scheme: salience-based
object proposal methods followed by post-classification using deep convolutional
features. In this work, we demonstrate that fully learnt, data driven proposal
generation methods can effectively match the accuracy of their hand engineered
counterparts, while allowing for very efficient runtime-quality trade-offs. This is
achieved by making several key improvements to the MultiBox method [4], among which
are an improved neural network architecture, use of contextual features and a new
loss function that is robust to missing groundtruth labels. We show that our
proposal generation method can closely match the performance of Selective Search
[22] at a fraction of the cost. We report new single model state-ofthe-art on the
ILSVRC 2014 detection challenge data set, with 0.431 mean average precision when
combining both Selective Search and MultiBox proposals with our postclassification
model. Finally, our approach allows the training of single class detectors that can
process 50 images per second on a Xeon workstation, using CPU only, rivaling the
quality of the current best performing methods.
