NDLI: Automatic drive annotation via multimodal latent topic model

Content Provider	IEEE Xplore Digital Library
Author	Bando, T. Takenaka, K. Nagasaka, S. Taniguchi, T.
Copyright Year	2013
Description	Author affiliation: DENSO Corp., Kariya, Japan (Bando, T.; Takenaka, K.) \|\| Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan (Nagasaka, S.; Taniguchi, T.)
Abstract	Time-series driving behavioral data and image sequences captured with car-mounted video cameras can be annotated automatically in natural language, for example, “in a traffic jam,” “leading vehicle is a truck,” or “there are three and more lanes.” Various driving support systems nowadays have been developed for safe and comfortable driving. To develop more effective driving assist systems, abstractive recognition of driving situation performed just like a human driver is important in order to achieve fully cooperative driving between the driver and vehicle. To achieve human-like annotation of driving behavioral data and image sequences, we first divided continuous driving behavioral data into discrete symbols that represent driving situations. Then, using multimodal latent Dirichlet allocation, latent driving topics laid on each driving situation were estimated as a relation model among driving behavioral data, image sequences, and human-annotated tags. Finally, automatic annotation of the behavioral data and image sequences can be achieved by calculating the predictive distribution of the annotations via estimated latent-driving topics. The proposed method intuitively annotated more than 50,000 pieces of frame data, including urban road and expressway data. The effectiveness of the estimated drive topics was also evaluated by analyzing the performances of driving-situation classification. The topics represented the drive context efficiently, i.e., the drive topics lead to a 95% lower-dimensional feature space and 6% higher accuracy compared with a high-dimensional raw-feature space. Moreover, the drive topics achieved performance almost equivalent performance texpressway datao human annotators, especially in classifying traffic jams and the number of lanes.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	2744
Ending Page	2749
File Size	1696657
Page Count	6
File Format	PDF
ISSN	21530858
DOI	10.1109/IROS.2013.6696744
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Visualization Acceleration Feature extraction Drives Hidden Markov models Image sequences
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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