SIFT algorithm-based Object detection and tracking in the video image

Recently, it's been a breeze to snap photos of high quality and good size, thanks to the widespread availability of low-cost but technologically advanced photo-capturing devices. A video is a series of still photographs taken at regular intervals. Since video can capture more of a scene over time, it can tell us more about our item in dynamic. A static backdrop frame is compared pixel-by-pixel with the current video frame for easy object recognition. The initial step of existing methods in this field is identifying the item of interest within the video sequences. Among the numerous challenges inherent in object tracking is selecting appropriate characteristics and models for locating and following the target item in a video. Intensity, form, color, and feature points are often chosen as relevant characteristics for categorizing visual objects. The methods of mean-shift trailing using the color pdf, optical drift surveillance using strength and motion, and SIFT (Scale inalterable feature transmute) tracking using scale-inalterable local feature points were all investigated in this research. Experimental findings thus far suggest that the chosen technique can successfully follow targets that undergo movement, rotating, motion blur, and distortion situations. For this reason, it is impossible to process movies manually. Therefore, a machine is required to analyze these movies. In this research, we make an effort to do just that via video object tracking. Technology and algorithms have advanced to the point that they can now automatically track the movement of an item inside a video clip.