动态多行人环境中基于最优交互避碰的机器人导航

在动态的多行人环境中,服务机器人仅依赖于自身传感器、以第一人称视角自主导航时. 机器人自主定位的不确定性以及对周围行人运动状态估计的不确定性均增加,这给机器人导航决策带来了困难. 为解决这个问题,提出一种基于最优交互避碰的机器人自主导航法. 本方法采用一种改进的粒子PHD滤波法即NP-PHDF法跟踪多个行人的状态. NP-PHDF法结合了卡尔曼粒子滤波及PHD滤波优点,因此它可以跟踪数目变化的多个目标,能够跟踪突然的加减速以及急转弯运动,并且能够抵抗遮挡. 同时,与基于粒子滤波的机器人自主定位法类似,NP-PHDF法使得行人运动状态的不确定性能够以粒子的分布来度量. 为降低状态估计的不确定性,本文提出一种“圈粒子”的粒子圈存法从粒子的分布中提取机器人和行人的真实状态. 算法的有效性在实际场景实验中得到了验证.     

基于改进粒子滤波的移动机器人行人跟踪

移动机器人对行人进行跟踪,是体现机器人智能的一个重要方面,具有广阔的发展前景和应用价值。然而环境的复杂性和行人运动的不确定性给行人的跟踪带来了极大的挑战。为此,在分析粒子滤波框架的基础上,对基本粒子滤波算法进行了两方面改进,提出了适用于移动机器人的行人跟踪方法。一方面在相似性估计阶段结合颜色信息、深度信息和社交力概念,提高了跟踪的精度;另一方面提出了二级粒子的概念,解决了粒子多样性缺失问题,提高了跟踪的准确度。在移动机器人turtlebot和公开数据集IAS-Lab上对改进的粒子滤波、序贯重要性重采样(sequential importance resampling,SIR)粒子滤波和扩展卡尔曼滤波(extended Kalman filter,EKF)算法进行对比,实验结果表明,改进的粒子滤波算法明显优于其他两种算法。     

基于可变形部件模型的粒子滤波快速行人检测与跟踪

针对视频中的行人检测和跟踪问题,提出一种基于可变形部件模型的快速行人检测、改进粒子滤波的行人跟踪算法。在行人检测阶段,为了改善非刚体行人的检测精度,采用了混合多尺度可变形部件模型;同时为了加速行人底层特征的计算,采用了基于预测算法的快速特征金字塔计算行人特征,代替传统的计算图像特征金字塔的每一个尺度特征。在行人跟踪阶段,采用时变的状态空间模型和基于颜色梯度直方图的观测模型对检测到的行人进行跟踪。实验证明,改进的行人检测算法可以在性能损失忽略不计的条件下,大大提高检测速度,并且相对于传统的行人跟踪,改进的粒子滤波算法对行人这一非刚性目标能实现较好的跟踪。     

基于三维模型的粒子滤波行人跟踪算法

针对传统行人跟踪算法得到运动轨迹与真实轨迹差异巨大的问题,提出一种基于三维模型的粒子滤波行人跟踪算法.该方法利用摄像机标定信息和图像帧信息建立行人的三维模型,解决图像中目标尺度的变化问题,并得到目标的真实运动轨迹.同时该方法利用双指数预测模型对粒子滤波算法进行优化,以解决短时遮挡问题,同时降低运算复杂度.实验表明,基于三维模型的粒子滤波行人跟踪算法能够较准确地建立行人三维模型,对比标准粒子滤波和KPF算法,能够对行人进行有效跟踪,对短时遮挡和尺度变化有较强的鲁棒性.     

一种融合粒子滤波的多特征特定行人追踪方法

针对特定行人目标跟踪,提出了一种融合粒子滤波的多特征特定行人检测追踪方法。该方法借鉴空间金字塔匹配模型将行人在空间上进行划分,然后融合颜色特征和局部三值模式（LTP）纹理特征,提取目标行人各子区域信息,最后结合粒子滤波来综合判断目标行人的位置。实验结果显示,本文方法能够有效区分目标与背景,同时在目标行人被遮挡的情况下,算法能够有效跟踪。      

MB-LBP特征提取和粒子滤波相结合的运动目标检测与跟踪算法研究

在复杂环境下,由于行人密度大以及运动随机性,导致运动目标(行人)难以检测和跟踪,造成人员计数误差。提出一种MB-LBP(Multi-scale Block Local Binary Pattern)特征提取和粒子滤波相结合的运动目标检测与跟踪算法来解决此问题。该算法首先用AdaBoost提取MB-LBP特征训练生成分类器进行人头检测,并根据人头目标尺寸变化范围去除部分误检,然后用改进的粒子滤波算法预测跟踪多个运动目标,最后对跟踪的运动目标进行计数。实验结果表明,提出的算法能够对复杂环境下多个运动目标进行有效检测及跟踪,准确、快速地对视频帧中的人员进行计数。     

基于激光雷达的舞蹈机器人室内行人跟踪方法

为使舞蹈机器人根据行人的运动轨迹进行路径的动态规划,增强与人共处的能力,提出一种基于激光雷达的室内行人跟踪方法。获取激光原始数据并进行预处理,根据激光数据的分布特点对DBSCAN算法进行优化,实现激光数据的快速聚类,完成环境分割,给出基于类簇到激光雷达的距离及行人身体宽度的行人识别方法,并将行人簇的位置作为初始跟踪位置,将激光数据图形化显示,激光数据转换成视频数据,利用粒子滤波算法实现行人跟踪并实时绘制轨迹。实验结果表明,该方法能获得较好的行人识别以及跟踪效果,且实时性较强。     

基于粒子滤波的弱目标检测前跟踪算法

针对低信噪比条件下机动目标的检测跟踪问题,提出了一种改进型的基于多模型的粒子滤波检测前跟踪算法.由于粒子退化问题,在目标信号微弱、目标发生机动或者信号幅值波动较强势,粒子滤波的TBD算法的检测概率和跟踪精度将会下降.本算法在粒子滤波的基础之上改进,即在每次循环之前加入新粒子,新粒子的分布是由平均法和前一时刻的目标估计结果进行确定.给出了粒子滤波的TBD算法推导以及数值计算过程.仿真实验表明:基于改进型粒子滤波检测前跟踪算法能够检测低信噪比的目标.     

基于HOG和颜色特征的粒子滤波行人跟踪算法的研究

行人跟踪技术是一种现代摄像预警技术,其能够起到对行人位置和动作的判定,并及时通过报警系统给予使用者相应的提示,能够有效避免过多的交通事故的发生。本文即是针对基于HOG和颜色特征的粒子滤波行人跟踪算法进行研究,对算法理论和算法描述进行分析,并对算法当中的HOG特征、颜色特征以及融合二者的粒子滤波算法进行了分析,同时针对于行人遮挡的检测情况进行了探讨,以期能为相关工作提供参考。     

基于时间序列模型的粒子滤波行人跟踪算法研究

针对行人运动的随机性导致运动状态模型适应性差和人在行走过程中可能发生短时全部或局部遮挡导致行人跟踪算法精度较低的问题,提出基于时间序列模型的粒子滤波行人跟踪算法;建立了行人运动时间序列模型;给出了基于对视频序列初始帧的检测,确定行人的位置、宽高等作为跟踪先验信息的方法;由先验信息计算加权颜色直方图构建初始粒子群分布,并利用时间序列运动模型预测粒子在下一时刻的状态分布,并更新粒子权值;根据有效粒子的个数判断是否进行重采样;最后由所有粒子的加权和估计行人的运动状态;仿真实验表明:文中提出根据行人的运动轨迹时间序列运动模型可使行人的状态估计更准确,预测误差进一步减小,预测精度得到了提高.     

基于箱粒子概率假设密度滤波的弱目标检测与跟踪算法

针对低信噪比条件下多弱小目标检测前跟踪算法跟踪效率低、计算复杂度高等问题,提出一种基于箱粒子概率假设密度滤波的弱目标检测与跟踪算法.首先,针对由目标的贡献强度和噪声获得的目标强度量测图像,利用均值滤波抑制强度量测图像中的噪声;其次,以不交叉原则挑选出强度值较大区域作为区间量测;最后,利用箱粒子概率假设密度(BOX-PHD)滤波对上述所得的区间量测进行目标跟踪.仿真结果表明,所提出的方法可以提高跟踪性能,且计算效率高.     

具有动态弹性稀疏表示的鲁棒目标跟踪算法

目标跟踪问题中目标所在环境的变化对跟踪效果有较大影响.鉴于此,提出一种基于弹性网结构的稀疏表示模型,并在粒子滤波框架下设计一种应用稀疏表示模型的抗干扰动态弹性网目标跟踪算法.同时,设计一种根据环境变化程度动态更新稀疏表示模型参数的方法,以克服光照变化等干扰对算法跟踪质量的影响.此外,所提出算法通过使用各向异性核函数计算各候选区域为跟踪目标所在位置的概率,能够提高跟踪算法的准确性,并改进字典模板更新方法,确保模板更新的准确性与及时性,保证跟踪质量.经实验验证,所提出的动态弹性网跟踪算法与其他跟踪算法相比,在光照等扰动下具有更好的跟踪效果,在遮挡及快速运动等情况下也能够有效保证跟踪精度.     

Improved infrared small target detection and tracking method based on new intelligence particle filter

Track‐before‐detect algorithm based on the particle filter algorithm has the problems of low tracking precision, poor particles, and requiring a large amount of particles to be calculated in a low signal‐to‐noise ratio, which is difficult to meet the accuracy and speed required by the modern infrared search and tracking system. In this paper, an improved infrared small target detection and tracking method based on a new particle filter is proposed. This is where particles are used to represent an individual bat to imitate the hunting process of bats. By adjusting loudness, frequency, and impulse emissivity of a particle swarm, the optimal particle at that time is followed to search in the solution space. In addition, the global search and the local search can also be dynamically switched to improve the quality and distribution of the particle swarm. The performance of the proposed algorithm is tested in a simulation scene and the real scene of the infrared small target detection and tracking. Experimental results show that the proposed algorithm improves the performance of the infrared searching and tracking system.     

Simultaneous people tracking and motion pattern learning

The field of Human Robot Interaction (HRI) encompasses many difficult challenges as robots need a better understanding of human actions. Human detection and tracking play a major role in such scenarios. One of the main challenges is to track them with long term occlusions due to agile nature of human navigation. However, in general humans do not make random movements. They tend to follow common motion patterns depending on their intentions and environmental/physical constraints. Therefore, knowledge of such common motion patterns could allow a robotic device to robustly track people even with long term occlusions. On the other hand, once a robust tracking is achieved, they can be used to enhance common motion pattern models allowing robots to adapt to new motion patterns that could appear in the environment. Therefore, this paper proposes to learn human motion patterns based on Sampled Hidden Markov Model (SHMM) and simultaneously track people using a particle filter tracker. The proposed simultaneous people tracking and human motion pattern learning has not only improved the tracking robustness compared to more conservative approaches, it has also proven robustness to prolonged occlusions and maintaining identity. Furthermore, the integration of people tracking and on-line SHMM learning have led to improved learning performance. These claims are supported by real world experiments carried out on a robot with suite of sensors including a laser range finder.     

车路视觉协同的高速公路防碰撞预警算法

目的 基于视觉的前车防碰撞预警技术是汽车主动安全领域的一个重要研究方向,其中对前车进行快速准确检测并建立稳定可靠的安全距离模型是该技术亟待解决的两个难点。为此,本文提出车路视觉协同的高速公路防碰撞预警算法。方法 将通过图像处理技术检测出来的视频图像中的车道线和自车的行驶速度作为输入,运用安全区实时计算算法构建安全距离模型,在当前车辆前方形成一块预警安全区域。采用深度神经网络YOLOv3（you only look once v3）对前车进行实时检测,得到车辆的位置信息。根据图像中前车的位置和构建的安全距离模型,对可能发生的追尾碰撞事故进行预测。结果 实验结果表明,重新训练的YOLOv3算法车辆检测准确率为98.04%,提出算法与马自达CX-4的FOW（forward obstruction warning）前方碰撞预警系统相比,能够侧向和前向预警,并提前0.8 s发出警报。结论 本文方法与传统的车载超声波、雷达或激光测距的防碰撞预警方法相比,具有较强的适用性和稳定性,预警准确率高,可以帮助提高司机在高速公路上的行车安全性。     

A Bayesian plan-view map based approach for multiple-person detection and tracking

This work proposes a novel approach for people detection and tracking in colour-with-depth sequences using a particle filtering approach. Detection and tracking are performed in plan-view maps integrating occupancy and height information with a novel plan-view map representation for colour information. Using the three maps, we propose a multiple particle filtering algorithm for people detection and tracking. The observation model proposed integrates information from the three maps so that people with different coloured clothes are not confused even when they interact at close distances. To avoid the coalescence problem when people with similar coloured clothes approach each other, the weight of particles is modified by an interaction factor that combines colour and position information. The algorithm also avoids the coalescence problem in case of total occlusion by means of an occlusion detection and recovering mechanism. Finally, a solution is proposed to improve the exponential complexity of multiple particle filters so that the algorithm proposed has linear complexity.The approach proposed has been tested in several colour-with-depth sequences where people move and interact freely in the environment. In the sequences, people walk at different distances, cross their paths causing frequent occlusions, jump, run and have close interactions such as shaking hands or embracing each other. The experimental results show that our proposal is able to detect and keep track of every person with a low error and deals with partial and total occlusions. Besides, the detection and tracking techniques presented are appropriate for large tracking problems in real-time applications since their complexity is linear, are suitable for parallel processing and allow the integration of information provided by multiple stereo vision sensors.     

载波跟踪环路统计特性分析的欺骗检测方法

针对现阶段大部分卫星导航接收机跟踪阶段的欺骗检测方法只能检测单欺骗源发射的欺骗信号的问题,提出一种基于载波跟踪环路统计特性分析的欺骗检测方法。首先分析了跟踪阶段已有欺骗检测方法的不足;其次,建立了接收机正常接收信号模型和欺骗信号入侵后接收信号模型,对真实信号与欺骗信号的复合信号的统计规律进行了分析。理论分析表明,当欺骗信号与真实信号存在频差时,检测算法能够通过I路信号的幅度变化检测出欺骗信号。仿真结果表明,在接收机能接收到的正常载噪比范围内（28 dB·Hz~50 dB·Hz）,在2%的虚警概率下能够达到100%的检测概率。算法能够检测多欺骗源发射的欺骗信号,且检测性能比已有方法得到了提升（在载噪比相同的情况下,检测性能提升约1 dB;在干信比相同的情况下,检测性能提升约4 dB）。     

A dynamic optimization approach for adaptive incremental learning

A fundamental problem when performing incremental learning is that the best set of a classification system's parameters can change with the evolution of the data. Consequently, unless the system self‐adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses overtime. The approach incorporates various theories, such as dynamic particle swarm optimization, incremental support vector machine classifiers, change detection, and dynamic ensemble selection based on classifiers' confidence levels. Experiments carried out on synthetic and real‐world databases demonstrate that the proposed approach actually outperforms the classification methods often used in incremental learning scenarios. © 2011 Wiley Periodicals, Inc.     

Laser-based detection and tracking of multiple people in crowds

Laser-based people tracking systems have been developed for mobile robotic, and intelligent surveillance areas. Existing systems rely on laser point clustering method to extract object locations. However, for dense crowd tracking, laser points of different objects are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. This paper presents a novel and robust laser-based dense crowd tracking method. Firstly, we introduce a stable feature extraction method based on accumulated distribution of successive laser frames. With this method, the noise that generates split and merged measurements is smoothed away and the pattern of rhythmic swing legs is utilized to extract each leg of persons. And then, a region coherency property is introduced to construct an efficient measurement likelihood model. The final tracker is based on the combination of independent Kalman filter and Rao-Blackwellized Monte Carlo data association filter (RBMC-DAF). In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people on the height of 16 cm from horizontal ground. Evaluation with real data shows that the proposed method is robust and effective. It achieves a significant improvement compared with existing laser-based trackers. In addition, the proposed method is much faster than previous works, and can overcome tracking errors resulted from mixed data of two closely situated persons.     

Radar-based road-traffic monitoring in urban environments

This work presents a novel approach to object detection and tracking in urban environments using images obtained from a radar network, deployed in an urban environment. The proposed system detects, tracks and computes the speed of vehicles and generates alerts when vehicles exceed the predefined road speed limit. The available radar model is a low-cost device oriented to marine environments rather than terrestrial applications. For this reason, we emphasize in the development of a realistic, robust, efficient and effective algorithm which deals with the hardware limitations to provide a suitable overall performance. To reach this objective, we propose dual background subtraction model to detect objects and a tracking method based on the particle filter algorithm. Furthermore, to ensure real time restriction even in HD imagery, our method takes advantage in a natural way of multicore systems and exploits advanced SIMD capabilities available in last multicore processors families. Experimental results demonstrate that the proposed system is able to detect and track multiple objects and to provide speeding alarms when needed. It is also capable to handle target occlusions and disappearances derived from the radar limitations and the noisy urban environment.