On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Multi-sensor fusion: fundamentals and applications with software
Multi-sensor fusion: fundamentals and applications with software
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Multimedia surveillance systems
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Fusion-Based Background-Subtraction using Contour Saliency
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Object Tracking using Color Correlogram
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Modeling Quality of Information in Multi-sensor Surveillance Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Incorporating image quality in multi-algorithm fingerprint verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Background updating for visual surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Fusion of thermal infrared and visible spectrum video for robust surveillance
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computer Vision and Image Understanding
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A major challenge in real world object detection for Video Surveillance (VS) is the dynamic nature of environmental conditions with respect to illumination, visibility, weather change, etc. With increasing availability of cameras and other sensor modalities beyond visible spectrum at lower cost, multi-modal VS systems involving visible, thermal infrared cameras, etc., are seen as a promising solution for reliable and robust operation in unfavorable environmental conditions like at night or dark situation. However, there are several research challenges to actually utilize the combined benefits of using different modalities. This paper addresses the uncertainty problem in the fusion of the information provided by complementary modalities like visible spectrum and thermal infrared video in a generic framework using evidence theory. A belief model is developed to determine the validity of a foreground region detected by each source for tracking. Fuzzy logic modeling is done for generating belief mass function from the sensor information by using two measurement features. A novel algorithm is developed for dynamic assessment of individual sensor reliability within the belief model. A generic approach to re-assign the conflicting mass is adopted for belief fusion to be done in a weighted manner depending on the context. Finally, the confirmed objects are tracked and the sensor measurements of their position, size, etc., are fused using a weighted Kalman filter fusion method. The approach is evaluated by using a pair of visible and thermal infrared sensors in real world challenging scenarios.