Paper Info
Title
A browsing and retrieval system for driving data.
Abstract
With the increased presence and recent advances of drive recorders, rich driving data that include video, vehicle acceleration signals, driver speech, GPS data, and several sensor signals can be continuously recorded and stored. These advances enable researchers to study driving behavior more extensively for traffic safety. However, increasing the variety and the amount of driving data complicates the simultaneous browsing of various data and finding desired data from large databases. In this study, we develop a browsing and retrieval system for driving data that provides a multi-modal data browser, query-and similarity-based retrieval functions, and a fast browsing function that skips redundant scenes. For sharing data with several users, this system can be used via networks from PCs or smartphones, This system uses a time-series active search, which has been successfully used for fast search of audio and video data, as its retrieval function algorithm. In a few seconds, this system can retrieve driving scenes that are similar to an input scene from 80,000 scenes. Retrieval performance was compared in various retrieval conditions by changing the codebook size of the vector quantization for the histogram features and a combination of driving signals. Experimental results showed that more than 97% retrieval performance was achieved for driving behaviors of left/right turns and curves using a combination of such complementary information as steering angles and lateral acceleration. We also compared the proposed method to a conventional image-based retrieval method using subjective similarity scores of driving scenes. Our proposed system retrieved similar scenes with about a 75% retrieval performance that was five points higher than a conventional image-based retrieval method. It is because image-based method is sensitive to changes of image in the area except in the region of interest for driving data retrieval. The fast browsing function also skipped scenes that could not be skipped by an image-based method.
Year
DOI
Venue
2010
10.1109/IVS.2010.5547999
Intelligent Vehicles Symposium
Keywords
Field
DocType
image retrieval,query processing,time series,traffic information systems,vector quantisation,browsing function,codebook size,data sharing,driving data,histogram features,image based retrieval method,multimodal data browser,query based retrieval function,similarity based retrieval function,time series active search,vector quantization
Computer vision,Histogram,Computer science,Data retrieval,Data sharing,Image retrieval,Vector quantization,Artificial intelligence,Region of interest,Visual Word,Codebook
Conference
ISSN
Citations 
PageRank 
1931-0587
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Masashi Naito181.35
Chiyomi Miyajima234545.71
Takanori Nishino3389.13
Norihide Kitaoka427743.70
Kazuya Takeda51301195.60