Proceedings ArticleDOI
Adaptive background for real-time visual tracking
He Li,Daiqin Yang,Zhenzhong Chen +2 more
- pp 1-6
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TLDR
An adaptive background model (ABM) is proposed to realize real-time visual tracking with a high adaptivity and accuracy and uses the static background information to help tracking instead of only focusing on the target itself, especially when there are great appearance changes.Abstract:
Visual tracking plays a fundamental role in many applications, such as video surveillance, image compression and three-dimensional reconstruction. From the perspective of accuracy and complexity, correlation filter for target tracking has been proved to be one of the most efficient algorithms. However, it suffers from some difficulties when tracking complex objects with rotations, occlusions and other distractions. To improve its robustness, in this paper, we propose an adaptive background model (ABM) to realize real-time visual tracking with a high adaptivity and accuracy. We use the static background information to help tracking instead of only focusing on the target itself, especially when there are great appearance changes. Moreover, we use peak to side-lobe ratio to update the ABM. As shown in the experiments, our proposed method achieves effective performance in visual tracking with good tradeoff between computational complexity and accuracy.read more
Citations
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Journal ArticleDOI
Object Tracking on Satellite Videos: A Correlation Filter-Based Tracking Method With Trajectory Correction by Kalman Filter
TL;DR: A high-speed correlation filter (CF)-based tracker for object tracking on satellite videos that takes advantage of the global motion characteristics of the moving target in satellite videos to constrain the tracking process, which is achieved by applying a Kalman filter (KF) to correct the tracking trajectory of themoving target.
Journal ArticleDOI
A Four-Model Based IMM Algorithm for Real-Time Visual Tracking of High-Speed Maneuvering Targets
Edwards Ernesto Sánchez-Ramírez,Alberto J. Rosales-Silva,Jean Marie Vianney-Kinani,Rogelio Antonio Alfaro-Flores +3 more
TL;DR: This work proposes an interacting multiple model algorithm based on four kinematic models: constant velocity, constant acceleration, constant turn and thrust acceleration and results show that visual tracking is improved when using the proposed strategy.
Proceedings ArticleDOI
Human tracking by employing the scene information in underground coal mines
Xue Zhou,Kexin Chen,Qidong Zhou +2 more
TL;DR: A human tracking method by fusion of the scene information for underground coal mines environment, which aims to improve the tracking accuracy and robustness in the presence of complicated factors, and presents a pixel-to-region hierarchical bright spots removing criterion.
Journal ArticleDOI
High-Precision Visual-Tracking using the IMM Algorithm and Discrete GPI Observers (IMM-DGPIO)
TL;DR: This work proposes the integration of a bank of Discrete Generalized Proportional Integral Observers (DGPIO) within an Interacting Multiple Model (IMM) structure in order to improve the precision of visual-tracking tasks.
Proceedings ArticleDOI
Learning Cascaded Context-Aware Framework for Robust Visual Tracking
Ding Ma,Xiangqian Wu +1 more
TL;DR: A cascaded context-aware framework based on two networks that progressively model the foreground and background of the various targets over time and can generate the bounding box of the target flexibly is proposed.
References
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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.
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.
Book ChapterDOI
Exploiting the circulant structure of tracking-by-detection with kernels
TL;DR: Using the well-established theory of Circulant matrices, this work provides a link to Fourier analysis that opens up the possibility of extremely fast learning and detection with the Fast Fourier Transform, which can be done in the dual space of kernel machines as fast as with linear classifiers.
Proceedings ArticleDOI
Struck: Structured output tracking with kernels
TL;DR: This paper presents a framework for adaptive visual object tracking based on structured output prediction that is able to avoid the need for an intermediate classification step, and uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking.
Book ChapterDOI
Real-time compressive tracking
TL;DR: A simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis that performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.