Real-time pyramidal Lukas-Kanade tracker performance estimation

One of the effective ways to improve object tracking performance is a fusion of base tracking algorithms to their advantages and eliminate disadvantages. This fusion requires the estimation of the performances of the base object tracking algorithms. So the real-time estimation of the performance of each base tracking algorithm is required for the algorithm result to be used for the fusion. In this paper we propose an algorithm for performance estimation for the object tracking algorithm based on the pyramidal implementation of Lukas-Kanade feature tracker. The performance estimation is based on the analysis of the variations of the intermediate algorithm parameters calculated during object tracking, such as total and mean feature lifetime, eigenvalues, inter-frame mean square coordinate difference, etc. Different combinations of these parameters were tested to obtain the best evaluation quality. The statistic measures were calculated for the image sequence, one or two hundred frames long. These statistic measures are highly correlated with the algorithm performance measures, based on the ground truth data: tracking precision and the ratio of the false detected features. The experimental research was performed using synthetic and real-world image sequences. We investigated performance estimation effectiveness in different observation conditions and during image degradations caused by noise, blur and low contrast. The experimental results show good performance estimation quality. This allows Lukas-Kanade feature tracker to be fused with another tracking algorithms (correlation-based, segmentation, change detection) to obtain reliable tracking. Since the approach is based on the intermediate Lukas-Kanade algorithm parameters, then it does not bring valuable computational complexity to the tracking process. So real-time performance estimation can be implemented.