纽约大学的眼睛跟踪数据集(120图像)(York Univ Eye Tracking Dataset (120 images) )_图像处理_科研数据集
eye scene的原理
eye scene的原理
"Eye scene" 是指眼动追踪技术，它是一种用于研究和分析人
类视觉行为的技术。
它的原理基于人眼在观察场景时产生的微小运
动和变化。
眼动追踪技术通过追踪和记录眼球运动的轨迹，可以揭
示人在观察图像、视频或实际场景时的注意力分布、注视持续时间、扫视路径等信息。
眼动追踪技术的原理主要包括以下几个方面：
1. 眼球运动的生理特性，人眼在观察场景时会产生不同类型的
眼球运动，包括注视（眼球停留在特定位置）、扫视（眼球在场景
中移动以获取信息）和眨眼等。
这些眼球运动反映了人的视觉注意
力和信息处理过程。
2. 眼动仪的原理，眼动追踪技术通常使用眼动仪来记录眼球运
动轨迹。
眼动仪通过追踪眼球表面的反射或者通过红外摄像头捕捉
眼球的运动，然后将这些数据转换成眼动轨迹图或热度图，从而分
析人的视觉注意力和行为模式。
3. 数据分析和解释，眼动追踪技术还涉及对眼动数据的分析和
解释。
通过对眼动数据进行统计分析、热度图分析、区域注视分析等，可以揭示人在观察场景时的视觉注意力分布、信息处理路径和认知行为。
总的来说，眼动追踪技术的原理基于人眼在观察场景时的生理特性和眼动仪的记录功能，通过对眼动数据的分析和解释，可以深入了解人的视觉行为和认知过程。
这项技术在心理学、人机交互、广告营销等领域有着广泛的应用前景。
瞳孔和眼角的梯度特征重构快速定位算法
瞳孔和眼角的梯度特征重构快速定位算法胡旭;王兆仲【期刊名称】《计算机辅助设计与图形学学报》【年(卷),期】2015(000)012【摘要】In order to quickly extract the facial feature points, we propose a fast eye center and corner local-ization algorithm which is based on the reconstructed gradient feature. Studies have shown that gradient featureof human eyes is radiating distribution centered on pupil. Based on that, the algorithm proposed in this paper compute the center to detect the location of the pupil and the eye corners. Then necessary adjust-mentswith respect to the relative location of both pupil and eye corner is employed to eliminate the gross error. Only the original gradient information is used which is invariant to affine lighting changes. The images from both Muct and BioID database as well as low-resolution web camera are used to test the proposed al-gorithm. The experimental result demonstrate that the proposed algorithm has high detection precisionwith low computational cost, which can meet the demand of real-time applications.%为了快速提取人脸图像局部特征点，提出一种基于梯度特征重构的瞳孔和眼角快速定位算法。
middlebury 数据集评估指标 -回复
middlebury 数据集评估指标-回复Middlebury数据集评估指标是用于评估计算机视觉算法性能的一组标准，由Middlebury大学的Daniel Scharstein和Richard Szeliski开发。
该数据集广泛应用于光流估计、立体匹配和场景重建等计算机视觉任务的评估中，被视为此领域内的基准。
1. 数据集介绍：Middlebury数据集包含多个具有挑战性的场景，例如室内、室外、自然景观等，涵盖了多个光照条件和物体形状。
数据集提供了带有像素级标注的参考结果，用于评估算法的准确性和鲁棒性。
每个数据集都有一对输入图像，通常包括参考图像和未知图像（待估计结果）。
标注结果可以用于计算误差度量指标，评估算法的性能表现。
2. 评估指标：Middlebury数据集评估算法的性能使用一组常用的指标，包括平均角误差、平均端点误差和光流场密度。
- 平均角误差（Average Angular Error）：该指标用于评估光流估计算法的精度。
它计算了估计光流向量与真实光流向量之间的角度。
通过对每个像素处角度误差的平均值进行计算，可得到整个图像的平均角误差。
- 平均端点误差（Average Endpoint Error）：该指标用于评估立体匹配算法和视觉里程计算法等任务的性能。
它测量了估计像素位置与真实位置之间的欧氏距离。
通过对每个像素处误差的平均值进行计算，可得到整个图像的平均端点误差。
- 光流场密度（Flow Field Density）：该指标用于评估光流估计算法的有效性。
它表示在光流场中存在有效光流向量的比例。
对于给定的图像，该值可以帮助衡量算法在各个区域的光流估计能力，以及是否存在无效的或噪声影响的光流向量。
3. 评估过程：针对Middlebury数据集，评估算法的过程可以分为以下几个步骤：- 数据准备：选择合适的数据集以及标注结果，确保数据集的多样性和代表性。
对于相应任务，例如光流估计，需要选择光流场标注的参考结果。
眼动仪的定义优缺点
Using eye tracking for usability testinga research note by Namahn Introduction (2)Definition (2)Eye-tracking technology (2)Eye tracking in usability testing (3)Difficulties, constraints and unresolved issues (4)Study results (5)Should we use eye tracking for usability testing? Some opinions (7)Commercial eye-tracking applications – prices and deliverables (8)Eye tracking in Belgium (11)Conclusion (11)References (12)IntroductionThe purpose of this research note is to explore how and if eye tracking should be used for usabilitytesting on intranets and Internet sites.DefinitionEye tracking is a technique allowing testers to determine eye movement and eye-fixation patterns of a person.Eye tracking can be used in both passive and active modes (3). In usability testing, eye tracking helps software designers to evaluate the usability of their screen layouts. It is an example of passive,“monitoring” use of eye tracking, because the eye-tracking devices simply monitor eye activity forlater study and analysis. Other examples of passive mode are:• Behavioural scientists can monitor what pilots or control room operators look at when given certain tasks or placed in certain situations.• Reading specialists can use eye tracking to recognize when a person is reading and when he/she has fixated on a word longer than normal, in order to create a highly individualized reading aid.• Marketing researchers can determine what features of product advertising and packaging attract buyer attention.Eye tracking can also be used to actively direct a computer through the motions of the eyes (active,“control mode”). Examples are:• Disabled people who cannot use their hands to operate a computer can do so with their eyes, using on-screen keyboards and mouse controllers.• Hospitals can provide an eye-aware communication program to people who have lost their ability to move and speak, either temporarily or permanently through a traumatic accident.Eye-tracking technologyThere are different ways of determining the direction of a person’s gaze (1). The “pupil-centre/corneal-reflection” method is probably the most effective and the most commonly used one.The method is based on the idea that the direction of a person’s gaze is directly related to the relative positions of the pupil and the reflection of an object off the cornea. This remote eye-tracking method does not require physical contact with the user’s eye or eye socket. It uses reflector trackers: a beam of light is projected onto the eye, after which a sophisticated camera picks up the difference between the pupil reflection and known reference points to determine what the user is looking at.So, this method involves the following procedures:1. A calibration procedure, enabling the eye-tracking system to learn about several physiologicalproperties of the tested person’s eye2. Illuminate the eye in order to reach the bright-pupil effect (cf. red-eye effect in flash photography), aneffect necessary to locate the centre of the pupil3. Measure the locations of the pupil centre4. Locate the relative position of the corneal reflection5. Calculate the direction of gaze (through image processing algorithms)Other eye-tracking methods make use of equipment such as skin electrodes or marked contact lenses.The “electro-oculography” method, for instance, involves measuring electric potential differencesbetween locations on different sides of the eye. However, remote eye tracking seems to be the most widespread method.Eye tracking in usability testingEye tracking versus traditional usability testing techniques>> © Eyetracking, Inc.: an eye-tracking sessionAt present, traditional usability testing still is widely used to determine the quality of web site and intranet design. Traditional testing usually involves participants having to perform a number of tasks while being observed by the experimenter. The following is measured:• Time to complete the task• Percentage of participants succeeding• Type and number of errors• Subjective ratings of ease of useSupporters of eye tracking claim that certain types of questions about usability are difficult to answer efficiently via traditional techniques. Suppose a user is spending longer than expected looking at an interface of a web site, without making the appropriate selection to reach his goal. A “think aloud”protocol or an interview afterwards won’t always reveal the reason for this failure. Different possible explanations will puzzle the experimenter:• Did the user overlook the hyperlink or button?• Was he distracted by another visual element in the interface?• Did he see the link or button, but fail to understand its purpose?• Did he look at the company branding elements?It is obvious that different answers to these questions would lead to different design solutions: e.g. in case of distraction, an unnecessary animated gif could be omitted. Without knowing the answers tothese questions, design recommendations however would have to be implemented by trial and error.In such cases, supporters of eye tracking claim, user’s eye movements can offer additional insight (4).Eye tracking: benefitsSo what relevant information can we get out of eye tracking? Eye movement recording can provide a record of the following:• The pattern of fixations (scan paths)• The time spent looking at various display elements• The deployment of visual attentionDuring the CHI 99 workshop “The hunt for usability: tracking eye movements” (4), a team ofworkshop participants focused on incorporating eye tracking in usability tests, and they came up witha “list of reasons for using eye tracking during usability tests:• Support other types of data• Help discriminate “dead time”• Measure how long a user looked at an area of interest• Capture a sequential scan path• Evaluate a specific interface• Extract general design principles• Demonstrate scanning efficiency• Understand expert performance for training• Help to sell usability testing• Provide a quantitative comparison of UI designs• Provide domain specific benefits (web pages, cockpits, text design)• Help explain individual differences”Difficulties, constraints and unresolved issuesConstraintsWhat can be possible difficulties, constraints of the eye-tracking method (13)?• Some persons cannot be eye tracked for physiological reasons: the pupil may not reflect enough light, or the iris may be too light in colour to be distinguished from the pupil reflection; the pupilmay be too large or it may be occluded by eye lashes or eye lids, making the eye difficult totrack; the person may have a wandering eye; etc.• Other persons cannot be eye tracked, for external reasons, such as eyeglasses or hard contacts.• Problems can occur during the eye-tracking process: a person’s eyes may dry out during an experiment and become difficult to track. Sometimes a participant can be eye tracked one dayand not the next, or half way through a test the eye track degrades to the point that the datacollected cannot be used.• While using a remote eye-tracking system, head movement may cause a delay until the eye tracker reacquires the eye, and may also cause loss of calibration. Restraining methods to keepthe participant’s head stationary may cause the participant to feel awkward and the testingsituation to become completely unnatural.• Interpreting eye-tracking data isn’t always easy, and can be an issue. Integrating eye-tracking data with data from traditional interviewing and observing methods can be even morechallenging.Negative consequences of these constraints are:• Restricting participants to those for instances without glasses limits the participant pool and may affect how representative the sample is.• The low success rate of gaining useful eye-tracking data from participants, and as a consequence the loss of participants (because of unacceptable data), means time and money wasted. Does thedata collected from just a few participants justify the cost?Unresolved issuesAt the CHI 99 workshop “The hunt for usability: tracking eye movements”, a team of participantsdiscussed technical issues with the eye-tracking technology. It suggested a number of issues that were considered to be “the most significant unresolved issues in the area of incorporating eye tracking inusability testing:• How to handle excessive volume of data• Correspondence of eye position and deployment of attention• Integration of multiple cameras• Fuse eye, mouse, facial expression, voice input, other data• Standardize definitions of derived data (e.g. fixations)• Taxonomy of dependent measures and applications• Reduce complexity of equipment use and data analysis• Techniques to deal with spurious data.”Study resultsA few examples of eye tracking illustrate what can be learned from eye tracking as a usability testingmethod. The first example is an example of commercial use of eye tracking, the second a scientificresearch on eye tracking, the third a case study for a customer service site. Some of the most striking results of both studies are listed.UIE, a User Interface Engineering company (1)What did usability testers of UIE learn about interface design via eye tracking?• Users typically look first in the centre, then to the left, then to the right of a web interface.• New and experienced web users scan essentially the same way. At first, the new user scans pages from left to right, as if reading a book. But he quickly changes to the centre-left-right sequence.• Users rarely look at the bottom of the page section that is visible, i.e. the area just above the browser’s status line.• Peripheral vision plays a significant role in what users see.The Stanford-Poynter Project (9)The Poynter Institute released an eye-tracking study of how people read news on the Web, mainly focusing on newspaper websites. The study led to conclusions such as:• Text attracts attention before graphics: headlines, briefs and captions get eye fixations first.Often, users only look at images during the second or third visit to a page.• Banner ads do catch online readers’ attention. For the 45 percent of banner ads looked at all, the tested people fixated on them for an average one second.• Users frequently alternate between multiple sites (interlaced browsing). So users are not focused on any single site.>> © Stanford-Poynter Project: illustration of the fixation order:Case study: the AT&T Customer Service Site (6)This study was based on new and existing users of the online service provided by AT&T, and illustrates an approach of usability testing that integrates state-of-the-art eye-tracking techniques with traditional interviewing methodology (integration of qualitative and quantitative approach). It focused on the functionality and usability of the home page of the site.Three essential characteristics of a website are usability, visibility, and optimisation. The eye-tracking analyses focused on measures of these three features:• Usability was measured by the time to succeed a task, the rate of success and the degree of confusion shown by the participants. Especially the degree of confusion (as displayed by new users) could be deducted from the eye gaze patterns.• Visibility: a second measure derived from patterns of eye movements indicates the extent to which all relevant regions of a display are noticed. Eye tracking enables experimenters todetermine if some regions were attracting too little attention while others are attracting too much. • Optimisation: eye tracking enabled the researchers to determine the number of non-page-changing extraneous menu items that were inspected (and then rejected) by the user. This number was used as a measure for efficiency: with optimal performance, a user would make few or no extra clicks on the menu items.>> © Eyetracking, Inc.: gaze-trace, indicating a high rate of confusionThe conclusion of the researchers was that the “combination of eye tracking with in-depthinterviewing produced strong corroborative results. Through this process, we were able to verify that what we interpreted as “confusions” were indeed confusions, and we were able to probe why theseconfusions occurred. This integration provided the research team with a richer interpretation thaneither technique alone. The result is a set of strong, detailed recommendations for site improvementthat have both statistical and qualitative bases.”Should we use eye tracking for usability testing? Some opinions…S. Schnipke and M. Todd, George Mason University, Virginia (13)“We believe (eye tracking) is still not a practical tool for most usability laboratories. The loss ofparticipants due to eye-tracking constraints requires the experimenter to allow more time for usability testing. This may place unacceptable constraints on a usability laboratory faced with a limited budget and a tight schedule. Because there are many different tools and methods available for researchers to collect data, we recommend that eye tracking only be used if there is no alternative method forcollecting the necessary data.Any laboratory that is considering a major purchase of an eye-tracking system should be aware of the ramp-up time associated with eye tracking and the likely data-collection success rates. Due to theabsence of “failure” data, it is important for researchers to report their rejection rates and let thecommunity know the problems they can expect to encounter if they decide to use an eye-trackingsystem. The experiences described in this study are typical of other tests we have conducted using eye tracing equipment. We do not believe that we are a unique case or our equipment is an exception and are the only ones to achieve such a low success rate. However, we do believe we are a unique case in reporting the challenges we have encountered with collecting eye-tracking data.”Jakob Nielsen, usability engineer, as quoted by Hakim (11)The level of usability testing reached in using eye tracking “is incredibly, exceedingly rare,” saidJakob Nielsen. (…) He said that while usability testing was common in many industries, he had heard of eye-tracking tests being conducted only at universities like Stanford, for Internet-related research, and by the Navy and a few technology companies.“There's no doubt it pays off dramatically, particularly for a site where people do a huge amount oftransactions,” Mr. Nielsen said. “Errors are a disaster for that type of system. The return on theinvestment is so huge that you can't even discuss the cost of building a lab. It's trivial.”Will Schroeder, Principal of UIE, User Interface Engineering (2)Schroeder has used eye tracking in several recent usability studies. He believes that “eye trackingdoes some things well – and others not at all”.According to Schroeder, eye trackers can:• Tell whether users are looking at the screen or not.• Differentiate reading from scanning for particular words or phrases.• Learn the relative intensity of a user’s attention to various parts of a web page.• Determine whether a user is searching for a specific item. Pupil diameter appears to increase when users are not sure what words they are looking for.• Compare users’ overall scan patterns.Eye trackers can not:• Let you know whether users actually “see” something.• Prove that users did not see something (Users can acquire information through peripheral vision).• Determine why users are looking at something.• Test everybody (there are certain constraints which make certain persons unfit to be tested).Alexei Oreskovic, journalist (10)“Many people associate usability testing with high-tech labs complete with two-way mirrors andfingernail-size audio-video equipment. All that's nice, but it's hardly essential. (…) High-tech eye-tracking equipment, which pinpoints exactly where a person's eyeball gazes on an individual Webpage, is another unnecessary luxury. It can be useful (a recent study by the Poynter Institute foundthat online newspaper viewers looked at text first and images later), but it's usually excessive.Frankly, it's more worth my while to go to the Web and read somebody else's research than it is tospend $50,000 and start an eye-tracking lab.”Karn, Ellis and Juliano, authors of a report on the CHI 99 workshop “The hunt forusability: tracking eye movements” (4).Karn, Ellis and Juliano believe that “incorporation of eye position recording into product usabilityevaluation can provide insights into human-computer interaction that are not available fromtraditional usability testing methods.” It also helps “reduce trial and error in user interaction design.”“All group members (of the workshop team that focused on incorporating eye tracking in usabilitytests) agreed that it is worth the time and effort to collect eye tracker data when the domain is wellunderstood.”Commercial eye-tracking applications – prices and deliverablesSome eye-tracking companiesEyetracking, Inc (ETI) is a provider of eye-tracking services. EyeTracking's patented technology can evaluate a user's experience while interacting with applications and Web sites. The company’s website offers information about deliverables and prices. It also has an interesting press corner.Veritest, a testing service provider, offers eye-tracking usability tests. It has several labs in NorthAmerica and Europe (Ireland, France), and has an alliance with Eyetracking, Inc.Eyetools, Inc. uses patented eye-tracking technology developed at Stanford University to produce improvements in web site design.Applied Science Laboratories (ASL) is an American provider of technology and systems for eye tracking, with distributors in Europe. They offer complete eye-tracking sets for usability testing. Such sets consist of a remote eye tracker, a magnetic head tracker, a camera with separate remote control, a portable computer, Asl software, etc. The set comes in a carry case that is light (under 15 lbs.) and that can easily fit in the overhead of an airplane.The Dutch company Testusability offers usability testing using an eye-tracking system called “EyeCatcher”. Other eye technology companies are: Erica Inc., LC Technologies Inc. and Fourward Technologies Inc.Example 1: Eyetracking, Inc sells eye-tracking usability testing:Deliverables are:• Real-time videos that show what users saw/missed: the videos show the point-of-gaze that is superimposed on the display images, providing a clear view of where the user was looking at every instant.• PowerPoint presentation of graphs and analyses:− “GazeSpots” show the relative amount of time spent by one or more users across a visual display. The intensity of use over the visual display is reflected by colourvariation.− “GazeStats” provide percentages of time spent in predefined regions of interest.− “GazeTransitions” show the transitions of the gaze from region to region (using numbers to indicate the order)− “GazeTraces” display the gaze of a single user over time as he or she works through a particular part of an interface (different colours indicate the amount of time spent oneach part)• DVD recording of all data and results• Written summary with recommendationsPricing:• ETI Concept Testing - $ 7500/Day• ETI WebCheck - $ 9500 (report available in 48 hours)• ETI Custom Website Evaluation – starting at $ 12,500• Pricing excludes travel-related expenses, respondent recruitment & incentives, and use of professional moderator for exit interviewsExample 2: LC Technologies, Inc. sells complete eye-tracking sets:LC Technologies, Inc. produces the “Eyegaze Development System”. Cost of such a Basic Development System in June 2001: $17,900 in U.S. Dollars. It consists of the following components:The first component is the Eyegaze Software Development Tool Kit: Calibration Program, Gaze Tracking Demonstration Program, Fixation/Saccade Analysis Function, Windows 2000 Professional, Microsoft Visual C++ Professional Edition Compiler, etc.The second component is the Eyegaze System Hardware:• Computer System (Modified for Integration with Eyegaze): Intel Pentium 3 Processor, 128 Megabyte Memory, 15" LCD Flat Panel Monitor (1024x768), Floppy Disk Drive, Hard Disk Drive (10GB), CD-ROM Drive, Video Frame Grabber Board• Monitor Support Arm with Camera Bracket• High-Speed Infrared Sensitive Camera and Lens (RS-170 or CCIR)• Infrared LED, Power Source and Filter• Eye-Image Video Monitor (Monochrome)• Power Surge Protector, Cables and Connectors• Optional: Portable Computer (in place of desktop computer): $1250 extraEye tracking in BelgiumIn Belgium, as an initiative of Hogeschool Antwerpen (Departement Ontwerpwetenschappen),Viewlab currently studies the usability of products, using the eye-tracking technique.>> © Viewlab: a head tracker (left) and the “I-scene system” (right)Siteview, a continuation of Viewlab, focuses on the usability of interfaces. Though still in a research phase, the Siteview project has already provide usability testing for two external clients. It uses theremote eye-tracking system called SMI (Sensomotoric Instruments) and can offer the followingdeliverables:• Videotape with the interface and a cursor overlay, indicating eye movements• Scan paths, the duration of fixations, the time spent in certain predefined “areas of interests”: this information is based on x-y-coordinates data.• Analysis of the eye-tracking data, resulting in a limited set of recommendations ConclusionThe conclusion of Namahn is that generally it considers the use of eye tracking as a method forusability testing to be a case of “overkill”. For a commercial web site for instance, it is in most cases exaggerated and even unnecessary. Usability testers will reach the same results simply by spendingtime on studying the current eye-tracking material available (research, studies, conferences) on theone hand, and other, general usability literature on the other hand.However, using eye tracking can be a good strategy if it is combined with traditional techniques and in very specific cases only. First, there is a quantitative argument in favour of eye tracking: it isworth the investment in case of very large usability testing budgets, of sites of which revenues depend largely on placing of advertising and type of advertising, and of systems that allow (or depend on)large transactions. In such cases, a minor improvement in usability will save a company a lot ofmoney, and it is a fact that a usability tester does get additional information when using the eye-tracking method and not limiting him to traditional testing methods.Another argument in favour of eye tracking is that it helps to sell usability testing. The eye-tracking method results in a series of numbers. It provides a quantitative, “scientific” basis for evaluating user behaviour; traditional usability testing is often reproached to lack such a basis. Thus, an integratedapproach, an approach that combines traditional interviewing and measuring techniques and eyetracking, is in some cases useful and advisable.References• Testing Web Sites with Eye Tracking (1)• What is Eye Tracking good for? (2)• Eyegaze Eyetracking Systems (3)• The Hunt for Usability: Tracking Eye Movements (4)• Eye Tracking Research & Applications Symposium 2000 (5)• How do your Users really Use your Site? Case Study of Eye Tracking for AT&T (6 - PDF)• The RealEYES Project – Usability Evaluation with Eye Tracking Data (7 – PDF)• Jakob Nielsen’s Alertbox, May 14, 2000: Eyetracking Study of Web Readers (8)• Stanford Poynter Project (9)• Testing 1-2-3 (10)• A High-Tech Vision Lifts Fidelity (11)• New Software Makes Eyetracking Viable: you can Control Computers with your Eyes (12)• “Trials and Tribulations of Using an Eye-tracking System”, article in CHI 2000, 16 April 2000, p.273-274 (13)• Eye to Eye (14)• (15)• The Eyes Have it (16)• Eye Movement Equipment Database: Choosing a System (17)Namahn bvbaGrenstraat 21B-1210 BrusselsTel. +32 (0)2 209 08 83© Namahn 2001。
视觉追踪训练的方法
视觉追踪训练的方法视觉追踪训练是一种用于计算机视觉领域的技术，旨在通过算法和模型的训练，使计算机能够准确地追踪和识别视频中的目标物体。
这项技术在许多领域都有广泛的应用，包括监控系统、自动驾驶、虚拟现实等。
视觉追踪训练的方法有很多种，其中一种常用的方法是基于深度学习的方法。
深度学习是一种模仿人脑神经网络结构的机器学习方法，通过多层次的神经网络模型来提取和学习图像特征。
在视觉追踪中，深度学习模型可以通过大量的标注数据进行训练，从而学习到目标物体的特征和运动规律。
视觉追踪训练的过程可以分为几个关键步骤。
首先，需要收集和准备用于训练的数据集。
这些数据集通常包括视频序列和目标物体的标注信息，标注信息可以是目标物体的边界框或者像素级别的标注。
接下来，需要选择合适的深度学习模型，常用的模型包括卷积神经网络（CNN）和循环神经网络（RNN）。
然后，将数据集输入到深度学习模型中进行训练，通过反向传播算法不断调整模型的参数，使其能够更好地预测目标物体的位置和运动轨迹。
最后，通过评估模型在测试数据集上的表现，可以对模型进行调优和改进。
视觉追踪训练的方法还包括一些技术手段和策略。
例如，为了提高模型的鲁棒性和泛化能力，可以采用数据增强技术，如随机旋转、缩放和平移等操作，来扩充训练数据集。
此外，还可以使用在线学习的方法，即在实时视频流中不断更新模型，以适应目标物体的外观和运动变化。
另外，还可以结合其他传感器信息，如激光雷达和红外传感器等，来提高追踪的准确性和鲁棒性。
视觉追踪训练的方法在实际应用中取得了显著的成果。
例如，在监控系统中，可以利用视觉追踪技术实现对目标物体的实时跟踪和识别，从而提高安全性和监控效果。
在自动驾驶领域，视觉追踪可以用于识别和跟踪其他车辆、行人和交通标志，以实现智能驾驶和交通管理。
在虚拟现实领域，视觉追踪可以用于实时渲染和交互，提供更加逼真和沉浸式的虚拟体验。
视觉追踪训练是一种重要的计算机视觉技术，通过深度学习模型的训练，可以实现对视频中目标物体的准确追踪和识别。
jhu icbm tracts模板
jhu icbm tracts模板
模板：JHU ICBM Tracts（Johns Hopkins University International Consortium of Brain Mapping Tracts）
ICBM Tracts是由约翰霍普金斯大学国际脑图协会（JHU ICBM）开发的一种脑白质纤维束分析工具。
该工具基于大量人体脑成像数据，可以精确地标记和可视化脑白质纤维束的解剖学路径和连接。
ICBM Tracts模板提供以下功能和特点：
1. 脑白质纤维束分析：ICBM Tracts可以对脑成像数据进行处理，提取出不同脑区域之间的脑白质纤维束，并通过特定的颜色和强度标记纤维束的路径。
2. 精确解剖学定位：ICBM Tracts基于大量脑影像数据集，使用统计模型和机器学习算法，可以准确地标记纤维束的解剖学位置，以帮助研究人员和医生更好地理解脑功能和疾病。
3. 可视化和交互性：ICBM Tracts提供直观的可视化界面，使用户可以自由旋转、放大和缩小三维脑图，从不同角度观察和分析脑白质纤维束。
4. 多模态数据支持：ICBM Tracts可以处理不同类型的脑成像数据，包括结构磁共振成像（MRI）和扩散张量成像（DTI）等。
5. 数据共享和合作：ICBM Tracts支持数据的导入、导出和共享，可以与其他研究人员进行合作和交流，促进脑科学研究的进展。
ICBM Tracts模板是一个功能强大的工具，为研究人员和医生提供了一种方便、准确和可视化的方式，来研究和理解脑白质纤维束的结构和功能连接。
该模板在脑科学、神经学、心理学等领域具有重要的应用和推广价值。
使用扫描束和多个检测器的眼睛跟踪[发明专利]
![使用扫描束和多个检测器的眼睛跟踪[发明专利]](https://img.taocdn.com/s3/m/cdea24facf2f0066f5335a8102d276a20129605b.png)
专利名称：使用扫描束和多个检测器的眼睛跟踪专利类型：发明专利
发明人：G·T·吉布森,J·O·米勒
申请号：CN201880025796.2
申请日：20180408
公开号：CN110520827B
公开日：
20220415
专利内容由知识产权出版社提供
摘要：本文所公开的示例涉及使用扫描束成像以及多个光检测器的眼睛跟踪。
一个示例提供了眼睛跟踪系统，该眼睛跟踪系统包括红外光源、配置为将来自红外光源的光跨包括用户角膜的区域的扫描的扫描光学器件、以及多个光检测器，每个光检测器被配置为检测以对应的角度从用户角膜反射的红外光。
申请人：微软技术许可有限责任公司
地址：美国华盛顿州
国籍：US
代理机构：北京市金杜律师事务所
代理人：赵林琳
更多信息请下载全文后查看。
LFW简介
wangzhiyang07@ 2014-3-25
文章信息
• LFW数据库由美国马萨诸塞大学（University of Massachusetts）阿姆斯特分校（ Amherst ）计 算机视觉实验室整理完成，对应文章为一篇技术 报告。
– 用于研究非受限情形下的人脸识别问题 – 已成为学术界（工业界）评价识别性能的benchmark
• 组建训练测试集合
LFW数据描述
13,233张图片 5,749个人 4,069个人只有一张图片 1,680个人多于一张图片
两种视角(1)
• View 1: Model selection and algorithm
– Training set: pairsDevTrain.txt 1100*2 – Test set: pairsDevTest.txt 500*2
数据获取流程
• • • • • • 原始图片收集 人脸检测 图片去重 标记人脸 裁切与尺寸归一化 组建训练测试集合
原始图片收集
• 源自CVPR04文章“Names and faces in the news”，流程如下： 从Yahoo News频道抓取新闻图片和对应的标题信息 人脸检测与聚类 带标签的人脸数据库(44,773)
NYU数据集预处理
NYU数据集预处理在做图像深度估计的时候⽤到了nyu数据集，该数据集包含彩⾊图，深度图，图像标签，相机位姿，可以⽤于图像分割，SLAM等，在使⽤之前，需要对该数据集做预处理，官⽅给的⼯具不太好⽤，这⾥⾃⼰⽤MATLAB写了个脚本，希望对⼤家能有所帮助，nyu数据集的下载地址是/~silberman/datasets/nyu_depth_v2.html，想更多地了解该数据集，可以参考其论⽂．clear;clc;nyuData = load('nyu_depth_v2_labeled');writeDirDepth = strrep(nyuData.rawDepthFilenames, '/', '\');[imageNum, ~] = size(writeDirDepth);for i = 1:imageNumwriteDirFile = writeDirDepth(i);writeDirFile = writeDirFile{1};filenameIndex = strfind(writeDirFile, '\');filename = writeDirFile(1:filenameIndex-1);writeDirFile = ['depths\', filename];cmd = ['mkdir ' writeDirFile];system(cmd);endwriteDirImage = strrep(nyuData.rawRgbFilenames, '/', '\');[imageNum, ~] = size(writeDirImage);for i = 1:imageNumwriteDirFile = writeDirImage(i);writeDirFile = writeDirFile{1};filenameIndex = strfind(writeDirFile, '\');filename = writeDirFile(1:filenameIndex-1);writeDirFile = ['images\', filename];cmd = ['mkdir ' writeDirFile];system(cmd);endwriteDirDepth = strrep(nyuData.rawDepthFilenames, '/', '\');[imageNum, ~] = size(writeDirDepth);for i = 1:imageNumwriteDirFile = writeDirDepth(i);writeDirFile = writeDirFile{1};filenameIndex = strfind(writeDirFile, '\');filename = writeDirFile(1:filenameIndex-1);writeDirFile = ['rawDepths\', filename];cmd = ['mkdir ' writeDirFile];system(cmd);endtemp = nyuData.depths(:, :, 1);imshow(temp,[]);temp = nyuData.images(:, :, :, 1);imshow(temp);writeDirImage = strrep(nyuData.rawRgbFilenames, '/', '\');[imageNum, ~] = size(writeDirImage);for i = 1:imageNumrgbImage = nyuData.images(:, :, :, i);writeDirFile = writeDirImage(i);writeDirFile = writeDirFile{1};writeDirFile = strcat('images\', writeDirFile);imwrite(rgbImage, writeDirFile, 'ppm');endimgRgb = imread(writeDirFile);imshow(imgRgb);writeDirDepth = strrep(nyuData.rawDepthFilenames, '/', '\');[imageNum, ~] = size(writeDirDepth);for i = 1:imageNumdepthImage = nyuData.depths(:, :, i);depthImage = uint16(round((depthImage./10)*65535));writeDirFile = writeDirDepth(i);writeDirFile = writeDirFile{1};writeDirFile = strcat('depths\', writeDirFile);imwrite(depthImage, writeDirFile, 'pgm');endimgDepth = imread(writeDirFile);imshow(imgDepth);writeDirDepth = strrep(nyuData.rawDepthFilenames, '/', '\');[imageNum, ~] = size(writeDirDepth);for i = 1:imageNumdepthImage = nyuData.rawDepths(:, :, i);depthImage = uint16(round((depthImage./10)*65535)); writeDirFile = writeDirDepth(i);writeDirFile = writeDirFile{1};writeDirFile = strcat('rawDepths\', writeDirFile);imwrite(depthImage, writeDirFile, 'pgm');endimgDepth = imread(writeDirFile);imshow(imgDepth);txtFile = fopen('association.txt', 'w');dirImage = nyuData.rawRgbFilenames;dirDepth = nyuData.rawDepthFilenames; [imageNum, ~] = size(dirDepth);for i = 1:imageNumnameImage = dirImage(i);nameImage = nameImage{1};nameDepth = dirDepth(i);nameDepth = nameDepth{1};fprintf(txtFile, '%s\t%s\r\n', nameImage, nameDepth); endfclose(txtFile);。
ANN MNIST手写数字识别总结
由于第十四周除了正常上课外，其余时间在整理机器学习的笔记，做中特社会调查报告，然后就是元旦放假，故第十四周没提交周报。
本周正常上课，继续完成老师都布置的课业任务，总结通信系统仿真结果，并且完成报告的撰写，分析社会调查结果，做好报告，查阅物理层安全方面的资料，翻译和整理论文。
其余时间是开始学习深度学习理论和编程实践，人工神经网络(ANN)和卷积神经网络，了解深度学习几个框架(Caffe 、Torch、TensorFlow、MxNet)，最主要还是TensorFlow，学习和查找了一下深度学习优化算法，并且利用人工神经网络做手写数字识别。
心得体会：第一个感受是时间过得很快，已然是15周了，要加快各方面进程。
神经网络从线性分类器开始，线性分类器是产生一个超平面将两类物体分开。
一层神经网络叫做感知器，线性映射加激励输出，每个神经元对输入信号利用激励函数选择输出，就像大脑神经元的兴奋或抑制，增加神经元数量、隐层数量，就可以无限逼近位置函数分布的形态，过多会出现过拟合算法。
ANN的学习方法是BP后向传播算法，其主要思想是每一层的带来的预测误差是由前一层造成的，通过链式求导法则将误差对每一层的权重和偏置进行求导更新权重和偏置，以达到最优结果。
因为ANN每一层神经元是全连接的，对于图像这种数据就需要非常大数量的参数，所以就出现了卷积神经网路CNN，利用一些神经元以各种模版在图像上滑动做卷积形成几张特征图，每个神经元的滑动窗口值不一样代表其关注点不一样，单个神经元对整张图的窗口权重是一样的即权值共享（这就是减少参数和神经元数量的原因），这么做的依据是像素局部关联性。
CNN主要有数据数据输入层、卷积计算层、激励层、池化层（下采样层）、全连接层、Batch Normalization层（可能有）CNN学习方法也是BP算法迭代更新权重w和偏置b。
CNN优点是共享卷积核，对高维数据处理无压力，无需手动选取特征，训练好权重，即得特征，深层次的网络抽取，信息丰富，表达效果好，缺点是需要调参，需要大样本量，训练最好要GPU，物理含义不明确。
基于图像处理的眼动追踪技术研究
基于图像处理的眼动追踪技术研究眼动追踪技术（Eye Tracking Technology）是一种基于图像处理的技术，旨在实时监测和记录人眼在二维或三维空间中的运动轨迹。
该技术在众多领域中具有重要的应用，如心理学、人机交互、市场营销等，其研究和应用价值备受关注。
眼动追踪技术的原理是通过不同的设备（如红外摄像机）或传感器（如鼻眼追踪系统）捕捉和跟踪眼睛的运动。
这些设备可以在眼睛周围放置，或者通过特殊的眼镜或眼镜架固定在眼睛上。
通过适当的算法和模型，可以准确地根据眼球的位置和运动来推断用户的视线方向，实现眼动追踪。
眼动追踪技术的研究主要集中在两个方面：单眼追踪和双眼追踪。
单眼追踪指的是通过追踪一个单独的眼球来获取眼动信息。
这种方法在追踪眼球位置和眼动轨迹时比较简单，但无法准确估计注视点。
双眼追踪则可以同时追踪两只眼球，实现更精确的注视点检测和视线跟踪。
目前，大多数眼动追踪系统都采用双眼追踪技术。
眼动追踪技术的研究和应用具有广泛的意义。
首先，在心理学领域，眼动追踪技术可以用来研究人眼在观看广告、阅读文字、浏览网页等任务时的注意力分配和认知加工过程。
这有助于深入理解人类的视觉注意机制和决策行为，为认知心理学的研究提供重要的实验数据。
其次，在人机交互领域，眼动追踪技术可以应用于交互界面的设计和优化。
通过分析用户的注视点和视线跟踪数据，可以确定用户在界面上的兴趣点和注意力分布，从而提供个性化的交互体验和改进用户界面的易用性。
此外，在市场营销和广告领域，眼动追踪技术可以用来研究消费者在购物过程中的注意力分配和行为决策。
通过分析用户在观看广告、商品展示和产品包装时的注视点和注视持续时间，可以评估广告效果和消费者的购买意愿，为市场营销策略提供有力的依据。
在现实生活中，眼动追踪技术已经开始应用于日常生活和医学领域。
例如，在驾驶员监控系统中，眼动追踪技术可以用来检测驾驶员的疲劳和注意力分散程度，提供驾驶辅助和安全警示。
眼动追踪技术在高校课堂教学中的应用研究
眼动追踪技术在高校课堂教学中的应用研究*杨东杰张岩郑伟博（中国石油大学(北京) 理学院，北京102249）摘要：近些年，眼动追踪技术发展迅速并已在诸多领域得以应用。
基于此，文章开展了眼动追踪技术在教学领域应用的研究，通过眼动追踪技术得到可视化的关注热点图及兴趣区域关注度，并以此为依据，文章分别研究了手机、投影仪及传统板书三种教学内容呈现方式及教室空间位置对学生注意力的影响，以及眼动追踪技术在实验教学中的应用。
实验结果表明，眼动追踪技术与教学的融合，能够为优选教学内容呈现方式、扩大教室“黄金区域”、开展对实验操作的过程性评价、提高教学质量、推进课堂革命提供有力支持。
关键词：眼动追踪技术；高校课堂；课堂教学；注意力【中图分类号】G40-057 【文献标识码】A 【论文编号】1009—8097（2020）02—0091—06 【DOI】10.3969/j.issn.1009-8097.2020.02.013课堂是教学的主战场，自陈宝生部长在《人民日报》撰文提出“课堂革命”之后，深化人才培养模式改革、提高教学质量、推进“课堂革命”成为了我国高等教育改革的焦点。
近年来，随着新一代智能技术的迅速发展，智能技术与课堂教学的深度融合也已成为“课堂革命”的核心驱动力，同时也给课堂教学带来新挑战[1]。
如何应用新技术、新方法为“课堂革命”服务，是当前亟需解决的问题。
为此，中国石油大学（北京）理学院将眼动追踪技术引入课堂，实现了对学生学习过程的可视化追踪。
一眼动追踪技术与应用1 眼动追踪技术眼动追踪（Eye Tracking）技术，也称为视线追踪（Gaze Tracking）技术，是利用光学、电子等技术手段，实时、客观、准确地记录被试当前的视线方向或视线落点位置的技术。
人们通过对所记录数据进行图像处理与分析，可以获取人对事物的主观看法，从而实现对人与事物之间的联系以及对事物的理解更科学的检测。
关于眼动的早期研究可以追溯到古希腊，但是真正使用仪器设备对眼动进行观察和实验的研究是从中世纪开始的，如阿拉伯人开展的“速示”呈现、色轮和视觉后象实验[2]。
tracking-by-detection中文
tracking-by-detection中文
Tracking-by-detection（追踪-检测）是计算机视觉中的一种重要任务，旨在对视频序列中的对象进行跟踪，并在时间上对其进行识别。
因此，这个任务通常被用于视频监视、行人跟踪、行为分析等场景中。
在追踪-检测任务中，重点在于如何建立一个准确而鲁棒的对象识别模型，以便将已经检测出来的对象与之前跟踪的对象进行匹配。
因此，检测技术作为这种跟踪方法的核心是不可或缺的。
具体地，追踪-检测方法可以分为两种：在线跟踪和离线跟踪。
在在线跟踪中，每次检测只基于已经跟踪的对象，而在离线跟踪中，则需要将检测结果和之前所有跟踪到的对象进行关联，并通过匹配来确定对象的跟踪。
在现代视觉系统和行为分析中，最常用于实现追踪-检测的方法是基于深度学习的方法。
一般采用卷积神经网络（CNN）这种深度学习模型来实现目标检测和跟踪。
其中，Yolo、Faster R-CNN、SSD等模型都是在此领域中比较流行的算法。
此外，还有一些其他的方法被用于追踪-检测的任务中。
例如，相关滤波（CF）、多目标跟踪（MOT）、粒子滤波（PF）等算法。
这些方法都可以优化跟踪中使用的特征和模型，以更好地实现目标检测和跟踪。
总之，对于追踪检测任务来说，准确的目标识别和跟踪是最重要的，因此不断地研究和探索新的算法和模型，以提高追踪-检测的精度和鲁棒性是非常重要的。
avenue数据集简介
avenue数据集简介
《avenue数据集》是一个用于异常检测的视频数据集，用于评估视频分析算法
的性能。
该数据集由来自纽约市街道的10个不同摄像头捕捉的多个视频序列组成，每个序列长达200帧。
这些视频序列包含了各种正常行为，例如车辆行驶、行人走动等，以及一些异常行为，如交通事故、突然停车等。
该数据集的目标是在复杂的城市环境中检测出异常事件。
它提供了一个基准测
试平台，用于评估和比较不同异常检测算法的性能。
通过使用这个数据集，研
究人员可以开发出更准确和鲁棒的异常检测算法，以提高城市监控系统的效果。
举个例子，一个视频序列可能包含正常的车辆行驶和行人走动，但在某一帧中
突然发生了一起交通事故。
这个异常事件可以通过分析视频中的移动模式、车
辆速度等特征来检测出来。
通过对这样的异常事件进行准确检测和识别，可以
帮助城市监控系统及时发现并应对潜在的危险情况。
基于人工神经网络的视线跟踪系统
基于人工神经网络的视线跟踪系统李兵;戴尔晗【期刊名称】《计算机技术与发展》【年(卷),期】2015(000)004【摘要】利用一种新的健壮的低像素的虹膜中心定位算法和眼睛角点检测算法，在摄像头像素不高的情况下，实时地提取出了注视屏幕时的眼睛特征，即虹膜中心和眼睛角点。
然后利用人工神经网络(ANN)，将人眼注视屏幕不同点时的眼睛特征进行分类，确定出人眼特征和屏幕上点的映射关系。
这种方法只需要一个普通的商业摄像头和一般的光照条件，并且允许小范围内的头部运动，很大程度上降低了系统的硬件成本，减少了使用者的限制条件，增强了系统的实用性。
实验结果表明，在普通的实验室光照条件下，这种方法在视线跟踪中达到了良好的效果。
%Use a new robust and low-resolution iris center localization algorithm and eye corners detection algorithm to extract the eye features of looking at the screen in real time under low-resolution camera conditions. Then classify the eye features of looking at different positions through the Artificial Neural Network ( ANN) to determine the mapping relationship of eye features and the screen points. This way only needs a commercial camera under the general illumination conditions and allows a small range of head motion,further more,it has reduced the hardware cost of the system and the restrictions of users,enhancing the system’ s practicability. Experimental results show that in the ordinary laboratory illumination conditions,this method achieves a good accuracy in the gaze tracking.【总页数】4页(P98-101)【作者】李兵;戴尔晗【作者单位】南京邮电大学自动化学院，江苏南京 210046;南京邮电大学自动化学院，江苏南京 210046【正文语种】中文【中图分类】TP391【相关文献】1.基于专家系统与人工神经网络集成的航姿系统故障诊断 [J], 王锋;孙秀霞2.有限元法在焊接工艺专家系统中的技术实现Ⅰ.基于人工神经网络的焊接工艺专家系统 [J], 李玉斌;谢志强;莫春立3.基于眼视线跟踪的打字系统在TMS320DM6446EVM上的实现 [J], 崔耀;何迪;于明轩;王军宁4.基于DM642的视线跟踪系统的实现与优化 [J], 刘瑞安;王廼琳5.基于专家系统和人工神经网络的计算机辅助工艺系统 [J], 俞进因版权原因，仅展示原文概要，查看原文内容请购买。
目标跟踪数据集
目标跟踪数据集目标跟踪数据集是指一组带有标注的视频数据，用于训练和评估目标跟踪算法的准确性和鲁棒性。
随着计算机视觉技术的发展，目标跟踪作为一项重要的研究领域，已经得到了广泛的关注和应用。
而构建目标跟踪数据集是开展相关研究工作的基础。
目标跟踪数据集通常涵盖了多个视频序列，每个视频序列都包含有一个或多个需要跟踪的目标。
通常情况下，每个目标都会被标注出来，以便算法可以在训练和测试过程中进行比对和验证。
数据集的构建通常需要人工标注，这是一项费时费力的任务。
为了保证数据集的准确性和可用性，标注人员需要仔细观察每个视频序列，并标注出目标的位置和边界框。
此外，还需要对视频序列进行质量控制和标注验证，以确保数据集的一致性和可靠性。
目标跟踪数据集的构建需要考虑多个因素。
首先，数据集的规模是一个重要的考虑因素。
数据集应该尽可能大，涵盖多个场景和各种目标类别，以保证算法的泛化能力。
其次，数据集的质量也很重要。
视频序列应该是清晰的、无噪声的，以保证算法可以准确地跟踪目标。
此外，数据集应该提供丰富的标注信息，如目标的位置、大小、姿态等，以满足不同目标跟踪算法对标注信息的需求。
目前，已经存在一些公开的目标跟踪数据集，如OTB-100、VOT等。
这些数据集已经被广泛应用于目标跟踪算法的训练和评估。
然而，由于目标跟踪领域的不断发展和算法的不断提升，一个更完善、更具挑战性的目标跟踪数据集仍然是一个迫切的需求。
在构建目标跟踪数据集时，需要考虑一些潜在的问题。
首先，由于目标跟踪是一个高计算复杂度的任务，数据集的规模可能会受到硬件和存储资源的限制。
其次，标注数据的准确性也是一个挑战。
标注人员可能会出现误差，导致数据集的质量下降。
另外，在收集视频序列时，还需要注意隐私保护和合规性等问题。
总的来说，构建一个好的目标跟踪数据集对于研究和应用目标跟踪算法至关重要。
一个准确、可靠且具有挑战性的数据集可以帮助研究人员评估和改进算法的性能，在实际应用中提供更精确的目标跟踪结果。