Managing Agent Interactions with Context-Driven Dynamic Organizations
Engineering Environment-Mediated Multi-Agent Systems
Trajectory representation using Gabor features for motion-based video retrieval
Pattern Recognition Letters
An automatic traffic surveillance system for vehicle tracking and classification
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A new approach for vehicle detection in congested traffic scenes based on strong shadow segmentation
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Road traffic model using distributed camera network
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
A comprehensive study of visual event computing
Multimedia Tools and Applications
Design of a hybrid object detection scheme for video sequences
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Maintaining trajectories of salient objects for robust visual tracking
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
MIS'05 Proceedings of the 11th international conference on Advances in Multimedia Information Systems
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This paper proposes an object segmentation and tracking algorithm for visual surveillance applications. In order to detect moving objects from a dynamic background scene which may have temporal clutters such as swaying plants, we devised an adaptive background update method and a motion classification rule. A two-dimensional token-based tracking system using a Kalman filter is designed to track individual objects under occlusion conditions. We propose a new occlusion reasoning approach where we consider two different types of occlusion: explicit occlusion and implicit occlusion. By tracking individual objects with segmented data, we can generate motion trajectories and set a motion model using polynomial curve fitting. The trajectory model is used as an indexing key for accessing the individual object in the semantic level. We also propose an efficient way of indexing and searching based on object-specific features at different semantic levels. The proposed searching scheme supports various queries including query by example, query by sketch, and query on weighting parameters for event-based retrieval. When retrieving an interested video clip, the system returns the best matching event in the similarity order. In addition, we implement a temporal event graph for direct accessing and browsing of a specific event in the video sequence