Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images
Blekinge Institute of Technology, School of Computing2009 (English)Conference paper (Refereed) Published
Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.
Place, publisher, year, edition, pages
Munster: Springer , 2009.
road sign, classification, supervised learning
IdentifiersURN: urn:nbn:se:bth-7955ISI: 000273458100148Local ID: oai:bth.se:forskinfo5C2249D7A13C5FD8C125762C005964CDISBN: 978-3-642-03766-5OAI: oai:DiVA.org:bth-7955DiVA: diva2:835636
13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009
Source: COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS Book Series: Lecture Notes in Computer Science Volume: 5702 Pages: 1220-1227 Published: 20092012-09-182009-09-092015-06-30Bibliographically approved