Proceedings ArticleDOI
Context-Aware Correlation Filter Tracking
Matthias Mueller,Neil Smith,Bernard Ghanem +2 more
- pp 1387-1395
TLDR
This paper reformulate the original optimization problem and provides a closed form solution for single and multi-dimensional features in the primal and dual domain and significantly improves the performance of many CF trackers with only a modest impact on frame rate.Abstract:
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.read more
Citations
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Journal ArticleDOI
Deep Learning for 3D Point Clouds: A Survey
TL;DR: This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
Proceedings ArticleDOI
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Heng Fan,Haibin Ling,Liting Lin,Fan Yang,Peng Chu,Ge Deng,Sijia Yu,Hexin Bai,Yong Xu,Chunyuan Liao +9 more
TL;DR: LaSOT is presented, a high-quality benchmark for Large-scale Single Object Tracking that consists of 1,400 sequences with more than 3.5M frames in total, and is the largest, to the best of the authors' knowledge, densely annotated tracking benchmark.
Book ChapterDOI
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
TL;DR: This work presents TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild, which covers a wide selection of object classes in broad and diverse context and provides an extensive benchmark on TrackingNet by evaluating more than 20 trackers.
Proceedings ArticleDOI
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
TL;DR: The spatial-temporal regularized correlation filters (STRCF) formulation can not only serve as a reasonable approximation to SRDCF with multiple training samples, but also provide a more robust appearance model thanSRDCF in the case of large appearance variations.
Posted Content
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan,Liting Lin,Fan Yang,Peng Chu,Ge Deng,Sijia Yu,Hexin Bai,Yong Xu,Chunyuan Liao,Haibin Ling +9 more
TL;DR: The LaSOT benchmark as discussed by the authors provides a high-quality benchmark for large-scale single object tracking, which consists of 1,400 sequences with more than 3.5M frames in total.
References
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Journal ArticleDOI
High-Speed Tracking with Kernelized Correlation Filters
TL;DR: 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.
Proceedings ArticleDOI
Online Object Tracking: A Benchmark
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.
Journal ArticleDOI
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.
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.
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.