Automated probabilistic estimation framework in real-time simultaneous objects tracking

Abstract

State-of-the-art in real-time simultaneous objects tracking through automated probabilistic estimation framework has been considered. The approach proposed here is dealt with in association with a novel self-correcting particle filter to track a number of moving objects. This idea is applicable to track most of simultaneous non-rigid objects, since 3D image is analyzed. Due to the fact that the captured frames are taken into account as two dimensional data matrices, some appropriate extracted features of the processed frames could be utilized to make the third dimension. The whole of suitable features of moving objects, which cannot directly be applied to the process of posterior probability calculation, need to be fed to a neural network for the purpose of making the third dimension. Subsequently, the probabilistic estimation of the present self-correcting particle filter in each frame is corrected through the neural network results to estimate each identified object, appropriately, in its current frame. The effectiveness of the proposed approach performance is guaranteed, once the results of three known particle filter-based procedures are taken into real consideration as benchmark approaches. Experimental results demonstrate that the proposed approach outperforms the traditional tracking systems for various challenging scenarios. It is shown that the accuracy of the proposed approach is improved, while its tracking error is correspondingly decreased.