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@article{144848,
        author = {Kiran Chaudhari and Santhosh Banoth},
        title = {Implementation of Real Time Moving Object Detection for Video Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {5},
        pages = {74-79},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144848},
        abstract = {In every real time object detection video system, pre-processing step includes moving object detection algorithm which identifies (extract) useful information of moving objects present in a video. Most algorithms of moving object detection require large memory space for storage of background related information, therefore the implementation of such algorithms becomes a difficult task as there are limited resources for embedded systems.Therefore, to overcome this limitation, in this paper we present an algorithm which optimizes memory use along with increasing speed and therefore performance and reliability of moving object detection scheme for video systems.The scheme being modified from original clustering-based moving object detection algorithm and has coded in Csharp is presented here.Results of the same were compared with the original clustering-based moving object detection and analyzed thoroughly on qualitative and quantitative basis.
The experimental results revealed that there is 11.66% reduction in memory requirement, hence speed has increased by 2% and therefore performance and reliability has increased by 4%, as compared to original without affecting accuracy and robustness.
},
        keywords = {Real Time Moving Object Detection, Video Surveillance System, Cluster Based Algorithm.},
        month = {},
        }