Improved infrared target-tracking algorithm based on mean shift

Wang, Zhile; Hou, Qingyu; Ling, Hao

doi:10.1364/ao.51.005051

Cited by 27 publications

(8 citation statements)

References 6 publications

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“…At present, most IR target tracking algorithms assume a known initial target location [1,3,14]. However, these are not available in most IR applications.…”

Section: Automatic Tracker Initializationmentioning

confidence: 99%

“…In PF approaches, IR target tracking is thought as a recursive Bayesian estimate problem, and the estimated states represent some parameters of the tracked targets, such as positions and velocity [13]. Equally popular are mean shift (MS) methods [14][15][16], which represent IR target appearances by kernel weighted gray histogram and use the mean shift procedure to identify the most likely position of the target in the next frame [17]. Both PF and mean shift methods rely on models of target appearance, which are used to locate the target as time evolves.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes

Wang

Chen

2015

Appl. Opt.

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Tracking infrared (IR) small targets is a vital component of many computer vision applications, including IR precise guidance, early warning, and IR remote sensing. Various complicated scenes, however, present significant challenges to the tracking task. To solve this problem, we present a novel 3D spatiotemporal difference-of-Gaussians (DoG) filter-based algorithm for tracking small targets in IR videos of various complex scenes. First, biologically inspired 3D DoG filters are proposed for IR small target tracking, which are capable of accounting for spatial and temporal information. Then, based on such filters, an effective and robust tracker is constructed to track the small targets, which are spatiotemporally distinguishable from background clutter. Extensive experiments show that our approach tracks the small targets accurately and robustly in realistic IR videos of complex backgrounds that present unique difficulties to other approaches.

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“…At present, most IR target tracking algorithms assume a known initial target location [1,3,14]. However, these are not available in most IR applications.…”

Section: Automatic Tracker Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes

Wang

Chen

2015

Appl. Opt.

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“…[1]. Besides, non-rigid targets have diverse postures, shapes, and sizes, such as humans and animals [2]. Combined with the imaging angles, scene clutters, background occlusions, and other factors [2–4], these could all be the constraints of infrared target recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, non-rigid targets have diverse postures, shapes, and sizes, such as humans and animals [2]. Combined with the imaging angles, scene clutters, background occlusions, and other factors [2–4], these could all be the constraints of infrared target recognition. Therefore, achieving robust target recognition with anti-interference ability (noise, fuzzification, occlusion, target shape, and scene changes) from an infrared image is still a challenging work.…”

Section: Introductionmentioning

confidence: 99%

Local structure preserving sparse coding for infrared target recognition

et al. 2017

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Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions.

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“…Most of the existing tracking algorithms can be formulated as optimization processes, which are typically tackled using either deterministic frameworks [1,[3][4][5][6] or stochastic frameworks [2,34]. Deterministic frameworks usually involve a gradient descent search to minimize a cost function.…”

mentioning

confidence: 99%