Fast object detection

Sadeghi, Mohammad Amin

Permalink

https://hdl.handle.net/2142/89047 Copy

Description

Title

Fast object detection

Author(s)

Sadeghi, Mohammad Amin

Issue Date

2015-12-04

Director of Research (if dissertation) or Advisor (if thesis)

Forsyth, David A

Doctoral Committee Chair(s)

Hoiem, Derek

Committee Member(s)

• Ramanan, Deva
• Lazebnik, Svetlana
• Golparvar-fard, Mani

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2015-12

Type of Resource

text

Permalink

http://hdl.handle.net/2142/89047

Copyright and License Information

Copyright 2015 Mohammad Amin Sadeghi

Owning Collections