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Object detection from registered visual and infrared sequences with the help of active contours

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2008
Yürük, Hüseyin
Robust object detection from registered infrared and visible image streams is proposed for outdoor surveillance. In doing this, halo effect in infrared images is used as a benefit to extract object boundary by fitting active contour models (snake) to the foreground regions where these regions are detected by using the useful information from both visual and infrared domains together. Synchronization and registration are performed for each infrared and visible image couple. Various background modeling methods such as Single Gaussian, Non- Parametric and Mixture of Gaussian models are implemented. For Single Gaussian and Mixture of Gaussian background modeling, infrared, color intensity and color channels domains are modelled separately. First of all, background subtraction is applied in the infrared domain in order to find the initial foreground regions and these are used as a mask for the foreground detection in the visible domain. After removing the shadows from the foreground regions in the visible domain, pixelwise OR operation is applied between the foreground regions of the infrared and visible couple and the final foreground mask is formed. For Non-Parametric background modeling, all domains are used altogether to extract foreground regions. For all background modelling methods, the resulting mask is used to get the final foreground regions in the infrared image. Finally, snake is applied to each connected component of the foreground regions on the infrared image for the purpose of object detection. Two datasets are used to demonstrate our results for human detection where comparisons against manually segmented human regions and against other results in the literature are presented.