Allerton 2015 Paper Abstract

Close

Paper ThB5.4

Lu, Yongxi (University of California, San Diego), Javidi, Tara (University of California, San Diego)

Efficient Object Detection for High Resolution Images

Scheduled for presentation during the Regular Session "Sparse Signal Processing" (ThB5), Thursday, October 1, 2015, 11:30−11:50, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on November 19, 2019

Keywords Image and Multimedia Signal Processing, Vision-Based Control

Abstract

Efficient generation of high-quality object proposals is an essential step in state-of-the-art object detection systems based on deep convolutional neural networks (DCNN) features. Current object proposal algorithms are computationally inefficient in processing high resolution images containing small objects, which makes them the bottleneck in object detection systems. In this paper we present effective methods to detect objects for high resolution images. We combine two complementary strategies. The first approach is to predict bounding boxes based on adjacent visual features. The second approach uses high level image features to guide a two-step search process that adaptively focuses on regions that are likely to contain small objects. We extract features required for the two strategies by utilizing a pre-trained DCNN model known as AlexNet. We demonstrate the effectiveness of our algorithm by showing its performance on a high-resolution image subset of the SUN 2012 object detection dataset.

 

 

All Content © PaperCept, Inc..


This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2019 PaperCept, Inc.
Page generated 2019-11-19  13:08:18 PST  Terms of use