Unmanned aerial vehicles

Robust Multi-Kernelized Correlators for UAV Tracking with Adaptive Context Analysis and Dynamic Weighted Filters

In recent years, the correlation filter (CF)-based method has significantly advanced in the tracking for unmanned aerial vehicles (UAV). As the core component of most trackers, CF is a discriminative classifier to distinguish the object from the …

Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking

Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects. Several approaches to enlarge search regions have been already proposed in the past years to make up for this shortcoming. However, with …

Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made …

Intuit Before Tuning: Type-1 and Type-2 Fuzzy Logic Controllers

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous …

Robust Scalable Part-Based Visual Tracking for UAV with Background-Aware Correlation Filter

Robust visual tracking for the unmanned aerial vehicle (UAV) is a challenging task in different types of civilian UAV applications. Although the classical correlation filter (CF) has been widely applied for UAV object tracking, the background of the …