Multiple stationary–non-stationary object-tracking approach in real-time applications through an extendable RGB modelling framework

The main contribution of the present research arises from developing standard object tracking approaches by considering a number of available research works in the area of multiple-object tracking approaches in real-time applications. An automatic robust algorithm is proposed in this investigation, which is able to track multiple synchronized stationary and/or non-stationary objects in high performance quality. The proposed approach is realized based upon an efficient extendable RGB (red/green/blue) modelling framework. This means that the investigated results are easily applicable to tracking the desirable number of objects to be chosen, i.e. the approach is flexible enough to reorganize to track the stationary/non-stationary objects in all frames of the video sequences. This proposed idea unique in that we plan to track all the chosen objects, in a synchronized manner, without a lengthy processing time. In fact, the proposed approach is realized in association with a desirable number of sub-systems, each of them needing to be enabled in parallel. The investigated results indicate that the present approach is now applicable in real-time applications by running a robust estimator to predict the new position of the tracked objects, since the objects are in the presence of long-term environmental problems – situations that are usually too difficult to deal with through standard object tracking algorithms.