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21 October 2015 Real-time object detection and tracking in omni-directional surveillance using GPU
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Recent technological advancements in hardware systems have made higher quality cameras. State of the art panoramic systems use them to produce videos with a resolution of 9000 x 2400 pixels at a rate of 30 frames per second (fps) [1]. Many modern applications use object tracking to determine the speed and the path taken by each object moving through a scene. The detection requires detailed pixel analysis between two frames. In fields like surveillance systems or crowd analysis, this must be achieved in real time. Graphics Processing Units (GPUs) are powerful devices with lots of processing capabilities for parallel jobs. The detection of objects in a scene requires large amount of independent pixel operations on the video frames that can be done in parallel, making GPU a good choice for the processing platform. This paper only concentrates on Background Subtraction Techniques [2] to detect the objects present in the scene. The foreground pixels are extracted from the processed frame and compared to the corresponding ones of the model. Using a connected- component detector, neighboring pixels are gathered in order to form blobs which correspond to the detected foreground objects. The new blobs are compared to the blobs formed in the previous frame to see if the corresponding object moved.
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Florian Depraz, Vladan Popovic, Beat Ott, Peter Wellig, and Yusuf Leblebici "Real-time object detection and tracking in omni-directional surveillance using GPU", Proc. SPIE 9653, Target and Background Signatures, 96530N (21 October 2015);

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