DocumentCode :
1752395
Title :
Development of a Semi-Automatic Data Annotation Tool for Driving Data
Author :
Torkkola, Kari ; Schreiner, Chris ; Gardner, Mike ; Zhang, Keshu
Author_Institution :
Motorola Labs, Tempe, AZ
fYear :
2006
fDate :
17-20 Sept. 2006
Firstpage :
642
Lastpage :
646
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. We present a semi-automatic annotation tool for this purpose that uses random forests as bootstrapped classifiers. The tool significantly reduces the manual annotation effort by enabling the user to verify automatically generated annotations, rather than annotating from scratch
Keywords :
driver information systems; very large databases; automobile sensor data; bootstrapped classifier; context aware driver assistance system; driving data; large annotated database; random forests; semiautomatic data annotation tool; Automobiles; Cameras; Context awareness; Databases; Infrared sensors; Page description languages; Radar tracking; Roads; Sensor systems; Traffic control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
Conference_Location :
Toronto, Ont.
Print_ISBN :
1-4244-0093-7
Electronic_ISBN :
1-4244-0094-5
Type :
conf
DOI :
10.1109/ITSC.2006.1706814
Filename :
1706814
Link To Document :
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