Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Interactive mining and semantic retrieval of videos
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Semantic retrieval of events from indoor surveillance video databases
Pattern Recognition Letters
A human-centered multiple instance learning framework for semantic video retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Moving vehicles detection based on adaptive motion histogram
Digital Signal Processing
A joint watermarking and ROI coding scheme for annotating traffic surveillance videos
EURASIP Journal on Advances in Signal Processing
Biologically-inspired multi-object tracking algorithm applied to traffic monitoring
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Vehicle classification from traffic surveillance videos at a finer granularity
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
| Hi-index | 0.00 |
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.