Impact Analysis of Type-2 Fuzzy Logic for Weighted Multiple Instance Learning Performance on Motion Blur and Low-Resolution Attributes

Abstract

Abstract—Type-2 fuzzy logic is an extension of type-1 fuzzy logic. Type-2 fuzzy logic can model an uncertainty better than type-1 fuzzy logic. Because it can model uncertainty well, type-2 fuzzy logic is very well used for decision making. In weighted multiple instance learning (WMIL), the tracker has not been able to decide whether the tracker has failed or not. In this paper, we analyze the influence of Type-2 fuzzy logic for WMIL performance on the motion blur and low-resolution attributes. Based on the experimental results, WMIL’s performance blur increasedby0.0725whileinthelow-resolutionattribute,WMIL’s performance decreased by -0.0045 when compared to WMIL without fuzzy logic type-2. The parameters used to analyze performance are success plot and precision plot. Index Terms—Type-2 fuzzy logic, weighted multiple instance learning, motion blur, low-resolution, object tracking.