Abstract
With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.
Highlights
Recent years have witnessed significant development in the field of computer vision.An enormous amount of research effort has gone into vision-based tasks, such as object tracking [1,2,3], recognition [4,5] and saliency detection [6]
Motion (CM),Apart full occlusion (FOC), illumination variation (IV), low resolution (LR), out sequences are affected by several adverse conditions such as background clutter (BC), camera of view (OV), partial occlusion (POC), similar object (SOB), scale variation (SV), viewpoint change motion (CM), occlusion (FOC), variation (IV), low resolution out ofinview (VC)
We propose a novel method to achieve robust aerial tracking
Summary
Recent years have witnessed significant development in the field of computer vision.An enormous amount of research effort has gone into vision-based tasks, such as object tracking [1,2,3], recognition [4,5] and saliency detection [6]. As an important field of computer vision, visual tracking [7,8,9,10,11] plays an active role in a wide range of applications, in which tracking using. Is widely applied to a diverse set of objects, which cannot be physically or persistently tracked from the ground, such as humans, animals, cars, boats, etc. Apart from those related to surveillance, a large number of new applications based on aerial tracking have been applied including infrastructure inspection [13], person following [14] and aircraft avoidance [15]. Compared with static tracking systems, aerial tracking requires the ability of analyzing a dynamic scene and handling new challenges posed on the UAV videos
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