{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-analysis-of-parallelized-motion-masking","title":"An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models","arxiv_id":"1702.05156","date":"2017-02-16","proceeding":null,"authors":["Peter Henderson","Matthew Vertescher"],"abstract":"Motion detection in video is important for a number of applications and\nfields. In video surveillance, motion detection is an essential accompaniment\nto activity recognition for early warning systems. Robotics also has much to\ngain from motion detection and segmentation, particularly in high speed motion\ntracking for tactile systems. There are a myriad of techniques for detecting\nand masking motion in an image. Successful systems have used Gaussian Models to\ndiscern background from foreground in an image (motion from static imagery).\nHowever, particularly in the case of a moving camera or frame of reference, it\nis necessary to compensate for the motion of the camera when attempting to\ndiscern objects moving in the foreground. For example, it is possible to\nestimate motion of the camera through optical flow methods or temporal\ndifferencing and then compensate for this motion in a background subtraction\nmodel. We selection a method by Yi et al. using Dual-Mode Single Gaussian\nModels which does just this. We implement the technique in Intel's Thread\nBuilding Blocks (TBB) and NVIDIA's CUDA libraries. We then compare\nparallelization improvements with a theoretical analysis of speedups based on\nthe characteristics of our selected model and attributes of both TBB and CUDA.\nWe make our implementation available to the public.","url_abs":"http://arxiv.org/abs/1702.05156v1","url_pdf":"http://arxiv.org/pdf/1702.05156v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-analysis-of-parallelized-motion-masking","repo_url":"https://github.com/Breakend/MotionDetection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"motion-detection","task_name":"Motion Detection"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}