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Content Provider | IET Digital Library |
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Author | Azab, Maha M. Shedeed, Howida A. Hussein, Ashraf S. |
Abstract | Object detection and tracking is an important task within the field of computer vision, because of its promising application in many areas, such as video surveillance. The need for automated video analysis has generated a great deal of interest in the area of motion tracking. A new technique is proposed for online object tracking-by-detection capable of achieving high detection and tracking rates, using a stationary camera, in a particle filtering framework. The fundamental innovation is that the detection technique integrates the local binary pattern texture feature, the red green blue (RGB) colour feature and the Sobel edge feature, using ‘Choquet’ fuzzy integral to avoid uncertainty in the classification. This is performed by extracting the colour and edge grey scale confidence maps and introducing the texture confidence map. Then, the tracking technique makes use of the continuous confidence detectors, extracted from those confidence maps, along with another three introduced classifier confidence maps, extracted from an online boosting classifier. Finally, both the confidence detectors and the classifier maps are integrated in the particle filtering framework, using the Choquet integral. Experimental results for both indoor and outdoor dataset sequences confirmed the robustness of the proposed technique against illumination variation and scene motion. |
Starting Page | 794 |
Ending Page | 803 |
Page Count | 10 |
ISSN | 17519659 |
Volume Number | 8 |
e-ISSN | 17519667 |
Issue Number | Issue 12, Dec (2014) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/8/12 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0238 |
Journal | IET Image Processing |
Publisher Date | 2014-07-16 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Automated Video Analysis Choquet Fuzzy Integral Classifier Confidence Maps Computer Vision Continuous Confidence Detectors Edge Detection Feature Extraction Filtering Method in Signal Processing Illumination Variation Image Classification Image Colour Analysis Image Motion Analysis Image Recognition Image Texture Indoor Dataset Sequence Local Binary Pattern Texture Feature Motion Tracking Object Detection Object Tracking Online Boosting Classifier Extraction Online Object Tracking-by-detection Outdoor Dataset Sequence Particle Filter Particle Filtering Framework RGB Colour Feature Scene Motion Sobel Edge Feature Extraction Stationary Camera Texture Confidence Map Video Signal Processing Video Surveillance |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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