Deep Learning Models for Context-Aware Object Detection

In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the corresponding scene. ContextNet is based on an intuitive idea of having cues about the general scene (e.g., kitchen and library), and changes the priors about presence/absence of some object classes. We provide a general means for integrating this notion in the decision process about the given ROI by using a pretrained network on the scene recognition datasets in parallel to a pretrained network for extracting object-level features for the corresponding ROI. Using comprehensive experiments on the PASCAL VOC 2007, we demonstrate the effectiveness of our design choices, the resulting system outperforms the baseline in most object classes, and reaches 57.5 mAP (mean Average Precision) on the PASCAL VOC 2007 test set in comparison with 55.6 mAP for the baseline.

Name thesis:degree_name

MS

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Arefiyan Khalilabad, Seyyed Mostafa

Chair dc:contributor.committeechair

• Abbott, A. Lynn

Committee members dc:contributor.committeemember

• Tokekar, Pratap
• Ramakrishnan, Naren

• Context-aware Detection
• Object Detection
• Context Modeling
• Context Extraction
• Convolutional Neural Network
• Computer Vision
• Deep learning (Machine learning)
• Machine learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:12772

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/88387