Abstract
With the development of deep learning, trackers based on convolutional neural networks (CNNs) have made significant achievements in visual tracking over the years. The fully connected Siamese network (SiamFC) is a typical representation of those trackers. SiamFC designs a two-branch architecture of a CNN and models’ visual tracking as a general similarity-learning problem. However, the feature maps it uses for visual tracking are only from the last layer of the CNN. Those features contain high-level semantic information but lack sufficiently detailed texture information. This means that the SiamFC tracker tends to drift when there are other same-category objects or when the contrast between the target and the background is very low. Focusing on addressing this problem, we design a novel tracking algorithm that combines a correlation filter tracker and the SiamFC tracker into one framework. In this framework, the correlation filter tracker can use the Histograms of Oriented Gradients (HOG) and color name (CN) features to guide the SiamFC tracker. This framework also contains an evaluation criterion which we design to evaluate the tracking result of the two trackers. If this criterion finds the SiamFC tracker fails in some cases, our framework will use the tracking result from the correlation filter tracker to correct the SiamFC. In this way, the defects of SiamFC’s high-level semantic features are remedied by the HOG and CN features. So, our algorithm provides a framework which combines two trackers together and makes them complement each other in visual tracking. And to the best of our knowledge, our algorithm is also the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result. Comprehensive experiments are conducted on the Online Tracking Benchmark (OTB), Temple Color (TC128), Benchmark for UAV Tracking (UAV-123), and Visual Object Tracking (VOT) Benchmark. The results show that our algorithm achieves quite a competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.
Highlights
Visual tracking is a very fundamental and important research topic in computer vision
This framework works on the basis of the evaluation criterion in (1) and can effectively utilize both the semantic features from convolutional neural networks (CNNs) and detailed texture features from traditional handcrafted feature extractors such as Histograms of Oriented Gradients (HOG) and color name (CN)
We proposed a novel tracking framework to explore the potential of combining the SiamFC tracker with other correlation filter- (CF-)based trackers, using the detailed texture features such as the HOG and CN to guide the high-level semantic features in CNN
Summary
Visual tracking is a very fundamental and important research topic in computer vision. In [11], the authors design a tracker which can efficiently adapt online information of the target’s appearance by learning a low-dimensional subspace representation incrementally Both the generative model and discriminative model are used in the tracking framework proposed by [19]. To the best of our knowledge, our algorithm is the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result (2) We design a novel tracking framework that combines the advantages of a SiamFC tracker and correlation filter trackers This framework works on the basis of the evaluation criterion in (1) and can effectively utilize both the semantic features from CNN and detailed texture features from traditional handcrafted feature extractors such as HOG and CN.
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