An intelligent estimation framework in the area of real-time tracking

Abstract

A number of referenced materials in the area of real-time tracking have been considered to present a novel intelligent estimation framework (IBEF). The main idea behind the research is to realize an approach that is applicable and efficient with respect to the final outcomes in the same way. The IBEF is realized, as a decision maker, in association with the neural network, where all the types of chosen objects, such as military, private vehicles, animals and other related objects, may be tracked in a set of frames of video through their approach. Then, the IBEF in each frame should be corrected to re-organize the estimation, in a constructive manner. The main goals may be chosen as solid, non-solid, stationary and non-stationary objects, as long as all of them can be taken in different quantities. The applicability of the approach can be presented in the areas of flight control, collision avoidance, etc. The process of realizing the approach can be continued by extracting some prominent features regarding the tracked objects, in order to apply them to the IBEF. The desirable performance of the proposed approach is guaranteed though a standard scenario. The results are finally compared with a benchmark approach, where the improvement of the approach performance can be verified.