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.
| Original language | English |
|---|---|
| Pages (from-to) | 154-167 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Intelligent Transportation Systems |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2003 |
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Keywords
- Advanced traffic management systems (ATMS)
- Advanced traveler information systems (ATIS)
- Background learning and background subtraction
- Intelligent transportation systems (ITS)
- Multimedia database indexing
- Segmentation
- Vehicle tracking
- Video analysis
ASJC Scopus subject areas
- Civil and Structural Engineering
- Electrical and Electronic Engineering
Cite this
Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems. / Chen, Shu Ching; Shyu, Mei-Ling; Peeta, Srinivas; Zhang, Chengcui.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 4, No. 3, 01.09.2003, p. 154-167.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems
AU - Chen, Shu Ching
AU - Shyu, Mei-Ling
AU - Peeta, Srinivas
AU - Zhang, Chengcui
PY - 2003/9/1
Y1 - 2003/9/1
N2 - 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.
AB - 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.
KW - Advanced traffic management systems (ATMS)
KW - Advanced traveler information systems (ATIS)
KW - Background learning and background subtraction
KW - Intelligent transportation systems (ITS)
KW - Multimedia database indexing
KW - Segmentation
KW - Vehicle tracking
KW - Video analysis
UR - http://www.scopus.com/inward/record.url?scp=1242277316&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=1242277316&partnerID=8YFLogxK
U2 - 10.1109/TITS.2003.821290
DO - 10.1109/TITS.2003.821290
M3 - Article
AN - SCOPUS:1242277316
VL - 4
SP - 154
EP - 167
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
SN - 1524-9050
IS - 3
ER -