NDLI: Urban traffic dense-stereo obstacle classification using boosting over visual codebook features

Content Provider	IEEE Xplore Digital Library
Author	Giosan, I. Costea, A.D. Nedevschi, S.
Copyright Year	2013
Description	Author affiliation: Comput. Sci. Dept., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania (Giosan, I.; Costea, A.D.; Nedevschi, S.)
Abstract	Every driving assistance system should have an obstacle classification module. Its main role is to accurately classify obstacles within a set of predefined classes. This paper presents a real-time dense-stereo based obstacle classification system that integrates visual codebook features like HOG, LBP and texton descriptor types in a powerful classifier. The system classifies the obstacles in four main classes: cars, pedestrians, poles/trees and other obstacles. The system acquires the image scenes using a pair of gray level stereo video-cameras. A combined approach using both 2D intensity and 3D depth information is firstly used for accurate obstacle segmentation. Then, the visual codebook features are extracted for a large set of obstacles with manually labeled classes and used for training a robust boosting classifier. The comparative classification results with an approach based on a random forest classifier trained on a relevant feature set show a considerable improvement, especially for the class of other obstacles.
Sponsorship	IEEE Romanian Sect.
Starting Page	111
Ending Page	116
File Size	355292
Page Count	6
File Format	PDF
ISBN	9781479914937
e-ISBN	9781479914944
DOI	10.1109/ICCP.2013.6646092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-05
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training boosting classifier Visualization Maximum likelihood detection visual codebook features Three-dimensional displays Nonlinear filters Vegetation Boosting dense stereo system obstacles classification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-feature real time pedestrian detection from dense stereo SORT-SGM reconstructed urban traffic scenarios

Real-time obstacle detection using dense stereo vision and dense optical flow

Obstacle and planar object detection using sparse 3D information for a smart walker

The classification of SSH tunneled traffic using maximum likelihood classifier

Segmentation of Nuclear Medicine three-dimensional images using Anscombe transformation

Image context classification based on visual codebook feature boosting

A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Real-time obstacle detection in complex scenarios using dense stereo vision and optical flow

Superpixel-based obstacle segmentation from dense stereo urban traffic scenarios using intensity, depth and optical flow information

Urban traffic dense-stereo obstacle classification using boosting over visual codebook features

Similar Documents

Multi-feature real time pedestrian detection from dense stereo SORT-SGM reconstructed urban traffic scenarios

Real-time obstacle detection using dense stereo vision and dense optical flow

Obstacle and planar object detection using sparse 3D information for a smart walker

The classification of SSH tunneled traffic using maximum likelihood classifier

Segmentation of Nuclear Medicine three-dimensional images using Anscombe transformation

Image context classification based on visual codebook feature boosting

A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Real-time obstacle detection in complex scenarios using dense stereo vision and optical flow

Superpixel-based obstacle segmentation from dense stereo urban traffic scenarios using intensity, depth and optical flow information

Urban traffic dense-stereo obstacle classification using boosting over visual codebook features