Abstract
Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning. First, we introduce basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews. Second, we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures. Then, we conclude with the general components of deep trackers. In this way, we systematically analyze the novelties of several recently proposed deep trackers. Thereafter, popular datasets such as Object Tracking Benchmark (OTB) and Visual Object Tracking (VOT) are discussed, along with the performances of several deep trackers. Finally, based on observations and experimental results, we discuss three different characteristics of deep trackers, i.e., the relationships between their general components, exploration of more effective tracking frameworks, and interpretability of their motion estimation components.
Highlights
Single object tracking is a fundamental and critical task in the fields of computer vision and video processing
To facilitate the development of single object tracking algorithms based on deep learning, in this work, we conclude with the general components of existing deep-learning-based tracking algorithms and present the popular components of deep neural networks, which are proposed for improving the representative ability of the features in Papers [29] [30] [31] [32] [33]
We find that since different components in the deep trackers have their special characteristics, improving only a single component sometimes cannot facilitate the tracking process
Summary
Single object tracking is a fundamental and critical task in the fields of computer vision and video processing. Benchmark 2013 (OTB-2013)[27] and Visual Object Tracking 2013 (VOT-2013)[28], have been proposed to evaluate the performance of these tracking algorithms With these developments, several papers reviewed the advancements and challenges in deep-learning-based tracking algorithms. To facilitate the development of single object tracking algorithms based on deep learning, in this work, we conclude with the general components of existing deep-learning-based tracking algorithms and present the popular components of deep neural networks, which are proposed for improving the representative ability of the features in. We present popular metrics used for evaluating the tracking performance on popular tracking datasets
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