运动平台下基于MAX金字塔和仿射运动的红外图像目标检测方法

针对运动平台下红外图像目标的检测,提出一个基于MAX金字塔和仿射运动模型的检测方法。表达图像信息的数据结构是成功地进行图像处理的关键,根据红外图像的特点,适应实时处理的要求,构造了一个新的图像金字塔结构--MAX金字塔。该金字塔的特点是速度快、易于用逻辑电路实现。用仿射运动模型描述平台的运动,通过基于MAX金字塔的仿射运动估计和对准,清除图像中平台的自运动信息。在残留图像中获得当前目标并补偿了平台运动后前一帧图像中目标的位置,从而检测出当前目标的位置和运动距离。     

基于灰度直方图的运动目标特征检测算法

为提高运动目标的检测效果和指导性，提出一种基于灰度直方图分析的运动目标特征检测算法。采用视觉成像技术进行运动目标图像采集和视觉特征分析，提取运动目标的动态视觉特征量。根据运动目标边缘差分变换和空间位置关系进行运动图像的特征分离，提取运动目标图像的边缘轮廓特征量。采用统计形状模型进行运动目标图像的二值化分离，构建运动目标图像的灰度直方图。根据灰度直方图中的统计信息进行目标特征检测和动态特征提取，实现运动目标图像的视觉检测和动态识别，有效提取运动目标的关键特征，实现目标特征检测。仿真结果表明，采用该方法进行运动目标图像的特征检测性能较好，对运动目标的动态识别能力较强。     

一种融合特征点与运动补偿信息的运动目标检测新算法

动态背景下的运动目标检测在汽车辅助驾驶、机器人导航等领域具有广阔应用前景。提出一种融合特征点运动信息和运动补偿信息的运动目标检测方法,解决以往基于单一特征,运动目标检测完整性差的问题,同时提高运动目标的检测准确率。首先,通过特征点检测跟踪,对图像进行分块运动估计,获得背景特征点的帧间运动模型,通过衡量特征点真实运动位置与运动模型的匹配程度,构建特征点的运动度量函数,从而获得特征点的运动信息。接着,利用背景特征点帧间运动模型,计算图像像素点的运动补偿图像,构建像素点的多帧运动补偿差异度量函数,从而获得像素点的运动补偿信息。最后,将特征点运动信息与运动补偿信息融合,获得运动目标检测结果。实验结果表明了该方法的有效性。     

基于红外相机和毫米波雷达融合的夜间行人检测

为了提升夜间环境行人检测的能力,使用红外相机和毫米波雷达进行信息融合.对两传感器数据进行时间配准并分别进行处理,利用改进YOLO算法处理红外图像得到目标类别特征,处理毫米波雷达数据获得目标的距离和速度特征,再建立空间对准模型对两传感器进行目标匹配,最后利用基于特征的融合算法完成夜间行人多模态信息输出.两传感器检测目标首次匹配之后,可以将类别信息反馈给雷达数据处理单元进行记录,当红外图像检测算法漏检时,利用雷达记录的信息补充输出类别特征.通过实验证明该融合算法提升了单一传感器行人检测成功率,在夜间场景具有良好的应用效果.     

运动背景下的运动目标检测方法

对运动目标检测问题,提出了一种运动背景下的运动目标检测新方法,为了配准图像首先利用傅立叶变换的相位相关法进行背景补偿,然后采用六帧相邻图像差分相乘的方法增强运动目标的像素点,拉大目标与背景残留噪声的差异,利用区域-分裂合并法进行目标分割,将运动目标检测出来;从而计算出运动目标的形心坐标,并绘出运动目标的运动轨迹.由于方...     

激光与单目视觉融合的移动机器人运动目标跟踪

针对室内环境下的移动机器人运动目标跟踪问题,提出一种基于激光与单目视觉传感信息融合的机器人定位和目标运动估计方法.首先,利用激光传感信息实现对目标的检测,并完成机器人定位与环境建图;然后,设计一种基于单目视觉传感器的目标位置估计算法,获得目标的距离和角度信息;为了实现两类传感信息的有效融合,将激光与单目视觉进行联合标定,得到二者的相对位姿关系,基于此,将激光与单目视觉提取的目标距离和角度通过具有最优重要性函数和权重的粒子滤波器进行融合,实现对目标运动状态的准确估计.实验结果表明该方法具有良好的跟踪性能.     

动态背景下的运动目标检测

针对动态背景下运动目标检测的问题,最大限度地降低背景对运动目标检测的影响,提出了一种基于相位相关法和傅里叶梅林变换的动态背景下运动目标检测算法.动态背景下运动目标检测的主要部分是背景运动补偿,首先利用相位相关法和傅里叶梅林变换估计全局运动参量,然后根据全局运动参量利用双线性内插法进行背景匹配,最后对配准后的图像利用帧间差分法提取运动目标.实验表明,该算法具有一定的鲁棒性,能有效地检测动态背景下的运动目标.     

复杂背景下运动点目标的检测和跟踪

为了解决复杂背景下运动点目标的检测和跟踪问题,本文提出了一种基于图像差分和聚类的运动目标检测和跟踪算法.该算法首先根据图像配准的方法,对序列图像进行差分运算,提取出候选的运动目标.在此基础上,利用运动目标在空间和时间上的相关性以及运动目标的轨迹所具有的连续性,采用一种特殊的聚类方法,从噪声环境中正确检测出运动目标的轨迹,并实现对运动目标的跟踪.实验表明该算法能快速检测出复杂背景下的运动点目标,并能有效处理轨迹相交和检测过程中出现新目标的情况.     

含运动目标的红外图像非均匀校正算法

通过分析运动目标对基于图像配准的非均匀校正算法校正效果的影响,基于相邻帧之间运动目标辐射强度不变的假设,提出含运动目标的红外图像非均匀校正算法。通过临近帧补全运动目标遮挡的场景,消除运动目标的影响,进行非均匀校正得到场景校正帧,再利用运动目标的辐射信息对各帧运动目标进行非均匀校正,最终得到全图校正帧。仿真和实测红外图像校正实验表明：算法能够很好地解决含运动目标的红外序列图像的非均匀校正问题。     

基于多视角航拍配准的运动小目标检测与跟踪

针对存在3D场景遮挡的航拍视频运动小目标跟踪问题,提出一种基于多视角航拍配准的运动小目标检测和跟踪算法。该算法首先对图像序列间隔采样,利用Harris检测器提取全局特征点,通过Delaunay三角网对待配准图像实现初始匹配,然后利用整合变换模型计算差分图像,并利用累积能量检测出目标,最后通过卡尔曼运动滤波消除运动目标跟踪的抖动。实验结果表明,该算法对城市和郊区场景的航拍视频可以检测出最小30个像素的缓慢运动目标。     

移动对象轨迹更新体系

在移动对象数据库中存在着移动对象位置的频繁更新,因而,如何管理移动对象轨迹（位置）的更新将成为移动对象数据库要解决的问题。对移动对象轨迹更新策略中存在的问题,提出了一个统一的移动对象轨迹更新体系。首先定义了移动对象的轨迹,从移动对象轨迹建模的角度,将移动对象轨迹的更新策略归纳为基于点与矢量的移动对象轨迹更新策略,给出了移动对象轨迹的更新原理及算法,并进行了两种更新策略的模拟实验比较。     

移动对象数据库索引研究综述

对近年来移动对象索引技术的发展进行了总结。根据移动对象索引空间的不同,将移动对象索引分为无限制空间移动对象索引以及网络空间移动对象索引,分析了过去、现在、未来移动对象索引技术的发展情况,最后对移动对象索引未来发展方向进行了讨论。     

Multisensor controlled robot system for recognizing and tracking moving multiple objects

This paper presents a cost-efficient, real-time vision-sensor system for identifying, locating and tracking objects that are unknown and randomly placed on a moving conveyor belt. The visual information obtained from a conventional frame-store unit and an end-effector based proximity sensor outputs are incorporated in a fuzzy-logic control algorithm to make the robotic manipulator grasp moving objects. The robot movements are going to be the result of the comparative measurements made by the sensors after the motion of the moving target is predicted and the gripper is brought into a zone close to the object to be grasped by the application of a vision system. The mobile object is traced by controlling the motion of the end-effector with an end-effector based infrared proximity sensors and conveyor position encoder by keeping the gripper's axis to pass through a median plane of the moving object. With this procedure and using the fuzzy-logic control, the system is adapted to pursue of a mobile object. Laboratory experiments are presented to demonstrate the performance of this system. ©1999 John Wiley & Sons, Inc.     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

Rectification of 3D data obtained from moving range sensor by using extended projective multiple view geometry

To measure the 3D shape of large objects, scanning by a moving range sensor is one of the most efficient methods. However, if we use moving range sensors, the obtained data have some distortions due to the movement of the sensor during the scanning process. In this paper, we propose a method for recovering correct 3D range data from a moving range sensor by using the multiple view geometry under projective projections in space-time. We assume that range sensor radiates laser beams in a raster scan order, and they are observed from two cameras. We first show that we can deal with range data as 2D images, and show that the extended multiple view geometry can be used for representing the relationship between the 2D image of range data and the 2D image of cameras. We next show that the extended multiple view geometry can be used for rectifying 3D data obtained by the moving range sensor. The method is implemented and tested in synthetic images and range data. The stability of the recovered 3D shape is also evaluated.     

On multiple moving objects

This paper explores the motion-planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.     

Target detection algorithm for dance moving images based on sensor and motion capture data

The effectiveness of classification based on motion capture data to identify the human skeleton dance types. The goal is based on the body joint's information is to perform the characteristic posture obtained by the ultra-high Kinect sensor, identifying for each dance. The proposed Target Detection (TD) algorithm is used to dance moving object identification based on the improved Gaussian Mixture Model (GMM). The proposed Target Detection (TD) algorithm based on a Gaussian Mixture Model is a widely used method for modeling background from a Kinect sensor moving objects. The used data set contains six folk dance sequences and their variations. Gesture recognition scheme using a plurality of time constraints, spatial information, and spatial distribution characteristics to create a training data set appropriate application. The Gaussian Mixed Model distribution background model account, the algorithm, and their frame difference can be extracted in straight lines to obtain target dance areas with less background and background photo station under motor damage conditions. Through real-time Target Detection (TD) algorithm dance moving images based on the Gaussian Mixture Model (GMM), these two algorithms effectively detect dance moving image targets.     

A framework for unsupervised mesh based segmentation of moving objects

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.     

一种自然环境下运动物体监测算法

提出了一种自然环境下运动物体的监测算法,该算法基于时空信息融合与特征识别,主要包括背景分析、前景提取、去除阴影、背景更新。其思想是将图像序列均转换为HSV颜色模型,并分析比较各像素点参数在某一时间段内的变化规律,通过判定公式的判定,便可区分出各像素点在某一帧中是属于背景点、运动物体点还是阴影点。该算法针对风、阳光、闪电等自然条件可能带来的影响进行了改进,并能够在光照突变、运动物体静止后融入背景、背景物体转为运动等情况下智能更新背景,适用于自然环境下运动物体的监测。