Files in this item



application/pdfSADEGHI-DISSERTATION-2015.pdf (18MB)
(no description provided)PDF


Title:Fast object detection
Author(s):Sadeghi, Mohammad Amin
Director of Research:Forsyth, David A
Doctoral Committee Chair(s):Hoiem, Derek
Doctoral Committee Member(s):Ramanan, Deva; Lazebnik, Svetlana; Golparvar-fard, Mani
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Fast Object Detection
Accurate Object Detection
Visual Phrases
Sentence Generation
Vector Quantization.
Abstract:The ultimate goal of computer vision is to understand images. We describe methods to understand images at two levels. One is at the level of description of images which we produce using sentences. These sentences talk about the things that are present in the image and about where they are and what they are doing. Then we ask in what ways should we describe images. We introduce visual phrases that are composite chunks of meaning. We show that object detectors could be better at detecting some visual phrases than detecting single objects. This process of image understanding needs to use a lot of detectors. Running conventional object detectors at the rate required for image understanding could be very slow. We study fast object detection from an engineering perspective. We argue that a desirable object detector must: (1) be able to work with legacy templates; (2) be random access; (3) be able to trade accuracy versus speed; (4) have any-time property. We describe a method to have all of these features together for a fast detector. We apply these techniques to deformable parts model object detectors and show two orders of magnitude speed-up while adding their desirable features. We finally investigate the consequences of this architecture with a view of improving convolutional neural networks.
Issue Date:2015-12-04
Rights Information:Copyright 2015 Mohammad Amin Sadeghi
Date Available in IDEALS:2016-03-02
Date Deposited:2015-12

This item appears in the following Collection(s)

Item Statistics