High-Speed Tracking with Kernelized Correlation Filters
TLDR
A new kernelized correlation filter is derived, that unlike other kernel algorithms has the exact same complexity as its linear counterpart, which is called dual correlation filter (DCF), which outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite being implemented in a few lines of code.Abstract:
The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.read more
Citations
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Journal ArticleDOI
Object Tracking Benchmark
TL;DR: An extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria is carried out to identify effective approaches for robust tracking and provide potential future research directions in this field.
Book ChapterDOI
Fully-Convolutional Siamese Networks for Object Tracking
TL;DR: A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.
Proceedings ArticleDOI
Accurate scale estimation for robust visual tracking
TL;DR: This paper presents a novel approach to robust scale estimation that can handle large scale variations in complex image sequences and shows promising results in terms of accuracy and efficiency.
Proceedings ArticleDOI
High Performance Visual Tracking with Siamese Region Proposal Network
TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
Proceedings ArticleDOI
ECO: Efficient Convolution Operators for Tracking
TL;DR: This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples.
References
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TL;DR: Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field.
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Tracking-Learning-Detection
TL;DR: A novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection, and develops a novel learning method (P-N learning) which estimates the errors by a pair of “experts”: P-expert estimates missed detections, and N-ex Expert estimates false alarms.
Proceedings ArticleDOI
Visual object tracking using adaptive correlation filters
TL;DR: A new type of correlation filter is presented, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame, which enables the tracker to pause and resume where it left off when the object reappears.