An optimized complex motion prediction approach based on a video synopsis

PurposeThe space-time variants algorithm will not give good results in practical scenarios; when no tubes increase, these techniques will not give the results. It is challenging to reduce the energy of the output synopsis videos. In this paper, a new optimized technique has been implemented that models and covers every frame in the output video.Design/methodology/approachIn the video synopsis, condensing a video to produce a low frame rate (FR) video using their spatial and temporal coefficients is vital in complex environments. Maintaining a database is also feasible and consumes space. In recent years, many algorithms were proposed.FindingsThe main advantage of this proposed technique is that the output frames are selected by the user definitions and stored in low-intensity communication systems and also it gives tremendous support to the user to select desired tubes and thereby stops the criterion in the output video, which can be further suitable for the user's knowledge and creates nonoverlapping tube-oriented synopsis that can provide excellent visual experience.Research limitations/implicationsIn this research paper, four test videos are utilized with complex environments (high-density objects) and show that the proposed technique gives better results when compared to other existing techniques.Originality/valueThe proposed method provides a unique technique in video synopsis for compressing the data without loss.

An Optimized Novel Technique for Video Synopsis Using Bayesian Object Tracking Algorithm

This paper presents another analytical video description of the methodology, which is far superior in pressure and depth to previous methodologies. In preparation, the video of the reconnaissance was usually packed by moving the pushing objects alongside the time hub, which undoubtedly resulted in a real crash and ordered issue antiques between the items being pushed. The main idea in this paper is that these antiques can be reduced by using the Bayesian calculation for fragmentation, and the last approach for foundation extraction is used following the products. We offer the best way to integrate these three heterogeneous activities into a single improvement system and achieve excellent outline performance. The Calculation of Metropolis does not like past methodologies that usually use optional improvements to fathom summary improvements to find the answer for our three-variable progress problem. A range of research demonstrates the feasibility of our technology.