scholarly journals Critical Overview of Visual Tracking with Kernel Correlation Filter

Technologies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 93
Author(s):  
Srishti Yadav ◽  
Shahram Payandeh
Keyword(s):  
Visual Tracking ◽  
Performance Metrics ◽  
Dynamic Properties ◽  
Feature Space ◽  
Correlation Filter ◽  
Circulant Matrices ◽  
Processing Technologies ◽  
Data Intensive ◽  
Novel Approach ◽  
Future Challenges

With the development of new methodologies for faster training on datasets, there is a need to provide an in-depth explanation of the workings of such methods. This paper attempts to provide an understanding for one such correlation filter-based tracking technology, Kernelized Correlation Filter (KCF), which uses implicit properties of tracked images (circulant matrices) for training and tracking in real-time. It is unlike deep learning, which is data intensive. KCF uses implicit dynamic properties of the scene and movements of image patches to form an efficient representation based on the circulant structure for further processing, using properties such as diagonalizing in the Fourier domain. The computational efficiency of KCF, which makes it ideal for low-power heterogeneous computational processing technologies, lies in its ability to compute data in high-dimensional feature space without explicitly invoking the computation on this space. Despite its strong practical potential in visual tracking, there is a need for an in-depth critical understanding of the method and its performance, which this paper aims to provide. Here we present a survey of KCF and its method along with an experimental study that highlights its novel approach and some of the future challenges associated with this method through observations on standard performance metrics in an effort to make the algorithm easy to investigate. It further compares the method against the current public benchmarks such as SOTA on OTB-50, VOT-2015, and VOT-2019. We observe that KCF is a simple-to-understand tracking algorithm that does well on popular benchmarks and has potential for further improvement. The paper aims to provide researchers a base for understanding and comparing KCF with other tracking technologies to explore the possibility of an improved KCF tracker.

Learning spatio-temporal correlation filter for visual tracking

2021 ◽  
Vol 436 ◽  
pp. 273-282
Author(s):  
Youmin Yan ◽  
Xixian Guo ◽  
Jin Tang ◽  
Chenglong Li ◽  
Xin Wang
Keyword(s):  
Visual Tracking ◽  
Temporal Correlation ◽  
Correlation Filter ◽  
Spatio Temporal

scholarly journals A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7543
Author(s):  
Bogdan Ilie Sighencea ◽  
Rareș Ion Stanciu ◽  
Cătălin Daniel Căleanu
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Evaluation Process ◽  
Surveillance Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Processing Technologies ◽  
Practical Applications ◽  
New Research

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

Characterization of Adaptive Magnetoelastic Metamaterials Under Applied Magnetic Fields

2016 ◽  
Author(s):  
Ryan L. Harne ◽  
Zhangxian Deng ◽  
Marcelo J. Dapino
Keyword(s):  
Magnetic Particle ◽  
Performance Metrics ◽  
Noise Control ◽  
Dynamic Properties ◽  
Dynamic Stiffness ◽  
Structural Vibration ◽  
Dynamic Force ◽  
Particle Alignment ◽  
The Impact

Whether serving as mounts, isolators, or dampers, elastomer-based supports are common solutions to inhibit the transmission of waves and vibrations through engineered systems and therefore help to alleviate concerns of radiated noise from structural surfaces. The static and dynamic properties of elastomers govern the operational conditions over which the elastomers and host structures provide effective performance. Passive-adaptive tuning of properties can therefore broaden the useful working range of the material, making the system more robust to varying excitations and loads. While elastomer-based metamaterials are shown to adapt properties by many orders of magnitude according to the collapse of internal void architectures, researchers have not elucidated means to control these instability mechanisms such that they may be leveraged for on-demand tuning of static and dynamic properties. In addition, while magnetorheological elastomers (MREs) exhibit valuable performance-tuning control due to their intrinsic magnetic-elastic coupling, particularly with anisotropic magnetic particle alignment, the extent of their properties adaptation is not substantial when compared to metamaterials. Past studies have not identified means to apply anisotropic MREs in engineered metamaterials to activate the collapse mechanisms for tuning purposes. To address this limited understanding and effect significant performance adaptation in elastomer supports for structural vibration and noise control applications, this research explores a new concept for magnetoelastic metamaterials (MM) that leverage strategic magnetic particle alignment for unprecedented tunability of performance and functionality using non-contact actuation. MM specimens are fabricated using interrelated internal void topologies, with and without anisotropic MRE materials. Experimental characterization of stiffness, hysteretic loss, and dynamic force transmissibility assess the impact of the design variables upon performance metrics. For example, it is discovered that the mechanical properties may undergo significant adaptation, including two orders of magnitude change in mechanical power transmitted through an MM, according to the introduction of a 3 T free space external magnetic field. In addition, the variable collapse of the internal architectures is seen to tune static stiffness from finite to nearly vanishing values, while the dynamic stiffness shows as much as 50% change due to the collapsing architecture topology. Thus, strategically harnessing the internal architecture alongside magnetoelastic coupling is found to introduce a versatile means to tune the properties of the MM to achieve desired system performance across a broad range of working conditions. These results verify the research hypothesis and indicate that, when effectively leveraged, magnetoelastic metamaterials introduce remarkably versatile performance for engineering applications of vibration and noise control.

scholarly journals Moving towards a reliable HIV incidence test – current status, resources available, future directions and challenges ahead

2016 ◽  
Vol 145 (5) ◽  
pp. 925-941 ◽  
Author(s):  
G. MURPHY ◽  
C. D. PILCHER ◽  
S. M. KEATING ◽  
R. KASSANJEE ◽  
S. N. FACENTE ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Critical Path ◽  
Population Level ◽  
Current Status ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Novel Approach ◽  
Product Profile ◽  
And Performance ◽  
The Impact

SUMMARYIn 2011 the Incidence Assay Critical Path Working Group reviewed the current state of HIV incidence assays and helped to determine a critical path to the introduction of an HIV incidence assay. At that time the Consortium for Evaluation and Performance of HIV Incidence Assays (CEPHIA) was formed to spur progress and raise standards among assay developers, scientists and laboratories involved in HIV incidence measurement and to structure and conduct a direct independent comparative evaluation of the performance of 10 existing HIV incidence assays, to be considered singly and in combinations as recent infection test algorithms. In this paper we report on a new framework for HIV incidence assay evaluation that has emerged from this effort over the past 5 years, which includes a preliminary target product profile for an incidence assay, a consensus around key performance metrics along with analytical tools and deployment of a standardized approach for incidence assay evaluation. The specimen panels for this evaluation have been collected in large volumes, characterized using a novel approach for infection dating rules and assembled into panels designed to assess the impact of important sources of measurement error with incidence assays such as viral subtype, elite host control of viraemia and antiretroviral treatment. We present the specific rationale for several of these innovations, and discuss important resources for assay developers and researchers that have recently become available. Finally, we summarize the key remaining steps on the path to development and implementation of reliable assays for monitoring HIV incidence at a population level.

scholarly journals Using Energy Dissipation Rate to Obtain Active Whitecap Fraction

2016 ◽  
Vol 46 (2) ◽  
pp. 461-481 ◽  
Author(s):  
Magdalena D. Anguelova ◽  
Paul A. Hwang
Keyword(s):  
Energy Dissipation ◽  
Dissipation Rate ◽  
Dynamic Properties ◽  
Length Distribution ◽  
Energy Dissipation Rate ◽  
Strength Parameter ◽  
Minimal Speed ◽  
Novel Approach ◽  
Decay Dynamic ◽  
Crest Length

AbstractActive and total whitecap fractions quantify the spatial extent of oceanic whitecaps in different lifetime stages. Total whitecap fraction W includes both the dynamic foam patches of the initial breaking and the static foam patches during whitecap decay. Dynamic air–sea processes in the upper ocean are best parameterized in terms of active whitecap fraction WA associated with actively breaking crests. The conventional intensity threshold approach used to extract WA from photographs is subjective, which contributes to the wide spread of WA data. A novel approach of obtaining WA from energy dissipation rate ε is proposed. An expression for WA is derived in terms of energy dissipation rate WA(ε) on the basis of the Phillips concept of breaking crest length distribution. This approach allows more objective determination of WA using the breaker kinematic and dynamic properties yet avoids the use of measuring breaking crest distribution from photographs. The feasibility of using WA(ε) is demonstrated with one possible implementation using buoy data and a parametric model for the energy dissipation rate. Results from WA(ε) are compared to WA from photographic data. Sensitivity analysis quantifies variations in WA estimates caused by different parameter choices in the WA(ε) expression. The breaking strength parameter b has the greatest influence on the WA(ε) estimates, followed by the breaker minimal speed and bubble persistence time. The merits and caveats of the novel approach, possible improvements, and implications for using the WA(ε) expression to extract WA from satellite-based radiometric measurements of W are discussed.

Bayesian Correlation Filter Learning with Gaussian Scale Mixture Model for Visual Tracking

2021 ◽  
pp. 1-1
Author(s):  
Yuan Cao ◽  
Guangming Shi ◽  
Tianzhu Zhang ◽  
Weisheng Dong ◽  
Jinjian Wu ◽  
...  
Keyword(s):  
Mixture Model ◽  
Visual Tracking ◽  
Correlation Filter ◽  
Scale Mixture ◽  
Gaussian Scale Mixture
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