In the field of UAV object tracking, correlation filter based approaches have received lots of attention due to their computational efficiency. The methods learn filters by the ridge regression and generate response maps to distinguish the specified …
Spatial regularization has proven itself to be an effective method in terms of alleviating the boundary effect and boosting the performance of a discriminative correlation filter (DCF) in aerial visual object tracking. However, existing spatial …
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate …
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 …
The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have …
In recent years, visual tracking is a challenging task in UAV applications. The standard correlation filter (CF) has been extensively applied for UAV object tracking. However, the CF-based tracker severely suffers from boundary effects and cannot …
Advanced communication technology of IoT era enables a heterogeneous connectivity where mobile devices broadcast information to everything. Previous short-range on-board sensor perception system attached to moblie applications such as robots and …
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 …
This paper presents an online adaptive tracker, which employs a novel weighted multiple instance learning (WMIL) approach.In the proposed tracker, both positive and negative sample importances are integrated into an online learning mechanism for …