Abstract
Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.
Highlights
Visual target tracking is an important topic in the field of computer vision
A blind deconvolution technique that used a piecewise linear model was introduced to estimate the unknown kernels. This method is combined with noise reduction technology that is based on wavelet multiframe decomposition and the peak signal-to-noise ratio (PSNR)
This section briefly introduces a tracking algorithm based on depth Features, a tracking algorithm based on a Siamese network and a target tracking algorithm based on an attention mechanism
Summary
Visual target tracking is an important topic in the field of computer vision. Its purpose is to accurately locate, identify and track the target after obtaining continuous images through the collector. An overview of research progress and visualization achievements at home and abroad reveals that visual target-tracking technology has unique social application value in terms of convenience, high efficiency, safety, reliability, high cost performance and low energy consumption [1] in the fields of medical diagnosis, human-computer interaction, public safety [2], video surveillance and posture estimation [3]. The differences of among aerial photography instruments, environments and target states, which lead to high information content, multiple heterogeneity and high dimensionality of aerial photography images or videos. Available image processing algorithms such as image denoising [4], image enhancement [5] and image mosaicking [6] can satisfy the real-time processing requirements of aerial image target recognition, but difficult problems and challenges remain in the realization of target tracking, including the following
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