Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems
Shu-Ching Chen
Mei-Ling Shyu
Peeta, S.
Chengcui Zhang
Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA;
This paper appears in: Intelligent Transportation Systems, IEEE Transactions on
Publication Date: Sept. 2003
Volume: 4,
Issue: 3
On page(s): 154- 167
ISSN: 1524-9050
INSPEC Accession Number: 7878926
Digital Object Identifier: 10.1109/TITS.2003.821290
Current Version Published: 2004-01-07
Abstract
One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.
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