基于改进提升模型的视频目标跟踪算法

为了减少视频目标跟踪中的累积误差,提出一种基于改进提升模型的视频目标跟踪算法。该算法结合样本有标签数据和无标签数据信息,基于半监督学习的思想,对有标签数据和无标签数据分别设计基于改进提升学习模型的分类器;将两个分类器进行加权组合,形成一个强分类器;将样本采集融合于目标跟踪的分类器学习中,有效解决了跟踪中随目标外观变化而造成的误差累积问题,提高了目标跟踪的鲁棒性。     

目标跟踪综述

随着深度学习与人工智能技术的不断发展,视频目标跟踪已经成为了计算机视觉的重要研究内容,在公安布控、人机交互、交通管制、军事等各个领域起到越来越重要的作用。尽管现在国内外学者提出了多种目标跟踪算法,也搭建了较为完善的目标跟踪系统,但是算法的鲁棒性依然是一个比较大的挑战。本文对运动目标跟踪系统结构进行了简要介绍,并从特征提取及融合、外观模型、目标搜索等方面详细阐述了目前主流运动目标跟踪算法。然后对目标跟踪算法在深度学习大环境下的新发展进行了分析,从基于深度学习的目标跟踪及目标检测算法角度分析了深度学习在提高目标检测算法鲁棒性方面的有效性,最后概述了深度学习在视频目标检测算法中的具体应用并对其未来发展进行了展望。     

深度学习在视频目标跟踪中的应用进展与展望

视频目标跟踪是计算机视觉的重要研究课题, 在视频监控、机器人、人机交互等方面具有广泛应用. 大数据时代的到来及深度学习方法的出现, 为视频目标跟踪的研究提供了新的契机. 本文首先阐述了视频目标跟踪的基本研究框架. 对新时期视频目标跟踪研究的特点与趋势进行了分析, 介绍了国际上新兴的数据平台、评测方法. 重点介绍了目前发展迅猛的深度学习方法, 包括堆叠自编码器、卷积神经网络等在视频目标跟踪中的最新具体应用情况并进行了深入分析与总结. 最后对深度学习方法在视频目标跟踪中的未来应用与发展方向进行了展望.     

基于区域协方差矩阵和2DPCA学习的视频跟踪方法研究

针对PCA在视频跟踪应用中需要将图像转换成向量而造成信息丢失和小样本等问题,提出一种基于2DPCA学习的自适应性视频跟踪方法。该方法将图像矩阵直接进行处理,保持了跟踪目标的空间结构信息。在粒子滤波框架下采用仿射变换运动模型,并通过协方差特征融合方式评估目标运动状态,提高了目标外观模型的学习能力,实现了鲁棒的自适应性跟踪效果。进行了标准的视频序列测试,结果证明提出的算法能够较好地适应目标姿态、光线和部分遮挡等跟踪问题。     

基于机器学习的目标跟踪算法研究综述

在视频目标跟踪研究中,基于机器学习的理论和算法成为了一个重要的发展方向。在线学习通过对样本持续的学习和更新从而适应背景环境以及目标的变化,能够获得更好的目标跟踪效果。根据算法的特点,将在线学习方法分为集成学习方法、判别式学习方法和核函数学习方法3类。重点对每类中具有代表性的几种方法进行详细描述,并分析其优缺点。最后还分析了机器学习方法在目标跟踪研究中面临的问题和未来的研究趋势。     

在线多目标视频跟踪算法综述

视频多目标跟踪是计算机视觉领域重要的研究课题之一,不论是在军用还是民用都有广泛应用。目前对单目标的跟踪算法研究已经相当成熟,但对于多目标跟踪的研究还处于发展阶段。重点研究了多目标跟踪过程中的四个重要阶段：特征提取、检测器、数据关联、跟踪器。特征提取阶段详细介绍了目前主流的特征提取方法以及各个方法之间的优缺点;检测器阶段首先详细介绍了目标外观模型在具体应用场景中的跟踪效果,接着对基于检测跟踪的多目标跟踪算法和基于深度学习的多跟踪算法进行了分析;跟踪器阶段分别介绍了目标运动模型的建立和利用不同跟踪器混合的多目标跟踪算法;数据关联阶段分别介绍了基于能量最小化的多目标跟踪以及常用的数据关联算法。接着,介绍了目前主流的数据集以及评测方法;最后对多目标跟踪未来的发展进行了思考和展望。     

基于多任务最小软阈值回归方法的目标跟踪*

在视频跟踪中,模型表示是直接影响跟踪效率的核心问题之一.在随时间和空间变化的复杂数据中学习目标外观模型表示所需的有效模板,从而适应内在或外在因素所引起的目标状态变化是非常重要的.文中详细描述较为鲁棒的目标外观模型表示策略,并提出一种新的多任务最小软阈值回归跟踪算法(MLST).该算法框架将候选目标的观测模型假设为多任务线性回归问题,利用目标模板和独立同分布的高斯-拉普拉斯重构误差线性表示候选目标不同状态下的外观模型,从而跟踪器能够很好地适应各种复杂场景并准确预测每一时刻的真实目标状态.大量实验证明,文中在线学习策略能够充分挖掘目标在不同时刻的特殊状态信息以提高模型表示精度,使得跟踪器保持最佳的状态,从而在一定程度上提高跟踪性能.实验结果显示,本文算法体现较好的鲁棒性并优于一些目前较先进的跟踪算法.     

基于深度学习的多目标跟踪关联模型设计

近年来, 深度学习在计算机视觉领域的应用取得了突破性进展, 但基于深度学习的视频多目标跟踪(Multiple object tracking, MOT)研究却相对甚少, 而鲁棒的关联模型设计是基于检测的多目标跟踪方法的核心.本文提出一种基于深度神经网络和度量学习的关联模型:采用行人再识别(Person re-identification, Re-ID)领域中广泛使用的度量学习技术和卷积神经网络(Convolutional neural networks, CNNs)设计目标外观模型, 即利用三元组损失函数设计一个三通道卷积神经网络, 提取更具判别性的外观特征构建目标外观相似度; 再结合运动模型计算轨迹片间的关联概率.在关联策略上, 采用匈牙利算法, 首先以逐帧关联方式得到短小可靠的轨迹片集合, 再通过自适应时间滑动窗机制多级关联, 输出各目标最终轨迹.在2DMOT2015、MOT16公开数据集上的实验结果证明了所提方法的有效性, 与当前一些主流算法相比较, 本文方法取得了相当或者领先的跟踪效果.     

视频跟踪算法研究综述

在许多计算机视觉应用领域中,视频跟踪是最基本的任务。尽管有了大量的跟踪算法,但是跟踪算法的鲁棒性仍是具有挑战性的问题。物体的突然运动、目标或者背景外观的改变、目标与目标以及目标与背景的遮挡、非刚性物体的结构、摄像机抖动等问题都是视频跟踪算法设计过程中需要考虑的因素。介绍了视频跟踪算法及其研究进展,综述了现有基本的目标跟踪算法分类,详细描述了每种表示方法,并指出其优缺点。进一步讨论了跟踪的重要性问题,包括目标检测、特征选择、贝叶斯跟踪、在线学习跟踪等。     

视频运动目标跟踪方法研究

视频运动目标跟踪属于计算机视频模块的重点研究内容,具备较大的应用前景。随着各种新技术融合到目标跟踪方法中,其跟踪准确性得到提升。受到目标形变、遮挡以及尺度变化影响,跟踪失败的问题也时有发生。为了改进视频运动目标跟踪方法,本文系统的阐述了当前视频运动目标跟踪方法的类型,从算法设计流程着手,给出关于视频运动目标跟踪方法的具体设计框架,对未来算法发展方向进行了展望。     

On the Complexity of Function Learning

The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range {0, 1}. Much less is known about the theory of learning functions with a larger range such as or . In particular relatively few results exist about the general structure of common models for function learning, and there are only very few nontrivial function classes for which positive learning results have been exhibited in any of these models.We introduce in this paper the notion of a binary branching adversary tree for function learning, which allows us to give a somewhat surprising equivalent characterization of the optimal learning cost for learning a class of real-valued functions (in terms of a max-min definition which does not involve any learning model).Another general structural result of this paper relates the cost for learning a union of function classes to the learning costs for the individual function classes.Furthermore, we exhibit an efficient learning algorithm for learning convex piecewise linear functions from ^d into . Previously, the class of linear functions from ^d into was the only class of functions with multidimensional domain that was known to be learnable within the rigorous framework of a formal model for online learning.Finally we give a sufficient condition for an arbitrary class of functions from into that allows us to learn the class of all functions that can be written as the pointwise maximum ofk functions from . This allows us to exhibit a number of further nontrivial classes of functions from into for which there exist efficient learning algorithms.     

Learning from a Population of Hypotheses

We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.     

Toward Efficient Agnostic Learning

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.     

The Power of Self-Directed Learning

This article studies self-directed learning, a variant of the on-line (or incremental) learning model in which the learner selects the presentation order for the instances. Alternatively, one can view this model as a variation of learning with membership queries in which the learner is only charged for membership queries for which it could not predict the outcome. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, monotone DNF formulas, and axis-parallel rectangles in {0, 1, , n – 1} ^d . These results demonstrate that the number of mistakes under self-directed learning can be surprisingly small. We then show that learning complexity in the model of self-directed learning is less than that of all other commonly studied on-line and query learning models. Next we explore the relationship between the complexity of self-directed learning and the Vapnik-Chervonenkis (VC-)dimension. We show that, in general, the VC-dimension and the self-directed learning complexity are incomparable. However, for some special cases, we show that the VC-dimension gives a lower bound for the self-directed learning complexity. Finally, we explore a relationship between Mitchell's version space algorithm and the existence of self-directed learning algorithms that make few mistakes.     

Transfer in variable-reward hierarchical reinforcement learning

Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs.     

一种新的连续动作集学习自动机

学习自动机（Learning automation,LA）是一种自适应决策器。其通过与一个随机环境不断交互学习从一个允许的动作集里选择最优的动作。在大多数传统的LA模型中,动作集总是被取作有限的。因此,对于连续参数学习问题,需要将动作空间离散化,并且学习的精度取决于离散化的粒度。本文提出一种新的连续动作集学习自动机（Continuous action set learning automaton,CALA）,其动作集为一个可变区间,同时按照均匀分布方式选择输出动作。学习算法利用来自环境的二值反馈信号对动作区间的端点进行自适应更新。通过一个多模态学习问题的仿真实验,演示了新算法相对于3种现有CALA算法的优越性。     

Context counts: How learners' contexts influence learning in a MOOC

Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs.     

不同程度的监督机制在自动文本分类中的应用

自动文本分类技术涉及信息检索、模式识别及机器学习等领域。本文以监督的程度为线索，综述了分属全监督，非监督以及半监督学习策略的若干方法-NBC(Naive Bayes Classifier)，FCM(Fuzzy C-Means)，SOM(Self-Organizing Map)，ssFCM(serni-supervised Fuzzy C-Means)gSOM(guided Self-Organizing Map)，并应用于文本分类中。其中，gSOM是我们在SOM基础上发展得到的半监督形式。并以Reuters-21578为语料，研究了监督程度对分类效果的影响，从而提出了对实际文本分类工作的建议。     

Exploring learnability between exact and PAC

We study a model of probably exactly correct (PExact) learning that can be viewed either as the Exact model (learning from equivalence queries only) relaxed so that counterexamples to equivalence queries are distributionally drawn rather than adversarially chosen or as the probably approximately correct (PAC) model strengthened to require a perfect hypothesis. We also introduce a model of probably almost exactly correct (PAExact) learning that requires a hypothesis with negligible error and thus lies between the PExact and PAC models. Unlike the Exact and PExact models, PAExact learning is applicable to classes of functions defined over infinite instance spaces. We obtain a number of separation results between these models. Of particular note are some positive results for efficient parallel learning in the PAExact model, which stand in stark contrast to earlier negative results for efficient parallel Exact learning.