An Analysis of Object Detection and Tracking Using Recursive and Non Recursive Algorithms for Motion Based Video

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In this study, it is proposed that a frame work evaluation of recursive and  non recursive algorithms for motion based video object detection and tracking. Object detection and tracking is a challenging task. Video based object detection systems rely on the ability to detect moving objects in video streams. There are many approaches adopted for video based object detection and tracking. Some of the factors should be considered such as stationary and non stationary background, deal with unconstrained environments, various object motion patterns and the  dissimilarity in types of object being detected and tracked. This study proposes a recursive and non recursive algorithms such as frame differencing, Mixture of Gaussians are used to detect the object in a motion based video through foreground and background separation. Next, for object tracking is made by Mean-Shift and Lucas Kanade optical flow (KLT) tracking algorithms are used. Based upon the video resolution and frame rate, the detection and tracking timings are calculated for the input video dataset. We observed that based on their evaluation to obtain correct detection and tracking, Recursive detection algorithm and Mean shift tracking  is used to track the detected objects in motion based video.
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Object Detection and Tracking; Foreground Background Segmentation; Mixture of Gaussians; Mean-Shift Tracking; Lucas Kanade Optical Flow (KLT)

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