Abstract
In this paper, a multiple object tracking method for visual surveillance applications is presented. Moving objects are detected by adaptive background subtraction and tracked by using a multi-hypothesis testing approach. Object matching between frames is done based on proximity and appearance similarity. a new confidence measure is assigned to each possible match. This information is arranged into a graph structure where vertices represent blobs in consecutive frames and edges represent match confidence values. This graph is later used to prune and refine trajectories to obtain the salient object trajectories. Occlusions are handled through position prediction using Kalman filter and robust color similarity measures. Proposed framework is able to handle imperfections in moving object detection such as spurious objects, fragmentation, shadow, clutter and occlusions. © 2005 IEEE.
Recommended Citation
F. Bunyak et al., "A Multi-Hypothesis Approach for Salient Object Tracking in Visual Surveillance," Proceedings - International Conference on Image Processing, ICIP, vol. 2, pp. 443 - 446, article no. 1530088, Institute of Electrical and Electronics Engineers, Jan 2005.
The definitive version is available at https://doi.org/10.1109/ICIP.2005.1530088
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-078039134-5
International Standard Serial Number (ISSN)
1522-4880
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2005
