Optimization Using Artificial Immune Systems Applied To Object Tracking And Segmentation

Abstract

This paper proposes the use of an artificial immune systems (AIS) to obtain the values of hyperparameters of networks such as the kernel parameter of the support vector machines (SVM) in object tracking and weighting factor of the loss term in object segmentation. The proposed iterative AIS method is generic to extend to other image processing tasks by formulating a corresponding objective function (fitness). We verify our method on the STRUCK method that uses SVM to track objects. Depending on feature variations between video frames, our AIS approach incorporates a complementary SVM model to select the SVM parameters for the main SVM model, where our AIS stopping criteria are classification accuracy and number of iterations. We then apply our AIS method to find the parameters that simultaneously minimize both false positives and false negatives of the object segmentation method Graph-Cut. Our results show that our AIS approach achieves significant enhancement of Graph-Cut segmentation accuracy and of STRUCK tracking quality.