SEMI-AUTOMATIC DRIVING DATA ANNOTATION

Author(s)
Torkkola, K. Schreiner, C. Gardner, M. & Zhang, K.
Year
Abstract

Data-driven approaches to constructing context aware driver assistance systems require large annotated databases of automobile sensor data. Manually annotating such large databases is costly and time-consuming. A semi-automatic annotation tool for this purpose that uses Random Forests as bootstrapped classifiers is presented. The tool significantly reduces the manualannotation effort and thus enables the user to verify automatically generated annotations, rather than annotating from scratch. For the covering abstract see E134653.

Request publication

17 + 0 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Publication

Library number
C 45492 (In: C 40997 CD-ROM) /91 / ITRD E136576
Source

In: Proceedings of the 13th World Congress and Exhibition on Intelligent Transport Systems (ITS) and Services, London, United Kingdom, 8-12 October 2006, Pp.

Our collection

This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.