Space-variant motion detection for active visual target tracking

A biologically inspired approach to active visual target tracking is presented. The approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal-frequency-based model of motion detection and the alignment of sensory-motor maps. Space-variant imaging is used to create a 1D array of elementary motion detectors (EMDs) that are tuned in such a way as to make it possible to detect motion over a wide range of velocities while still being able to detect motion precisely. The array is incorporated into an active visual tracking system. A method of analysis and design for such a tracking system is proposed. It makes use of a sensory-motor map which consists of a phase-plane plot of the continuous-time dynamics of the tracking system overlaid onto a map of the detection capabilities of the array of EMDs. This sensory-motor map is used to design a simple 1D tracking system and several simulations show how the method can be used to control tracking performance using such metrics as overshoot and settling time. A complete 1D active vision system is implemented and a set of simple target tracking experiments are performed to demonstrate the effectiveness of the approach.     

粒子滤波理论及其在目标跟踪中的应用

非线性估计领域的经典算法是扩展Kalman滤波（EKF）,它采用了Taylor展开的线性变换来近似非线性模型,因而存在计算量大、实时性差、估计精度低等缺点。而粒子滤波采用一些带有权值的随机样本（粒子）来表示所需要的后验概率密度,而不是采用传统的线性变换,从而得到基于物理模型的近似最优数值解,具有精度高、收敛速度快等特点。对经典的纯方位跟踪问题进行了仿真。仿真结果表明,粒子滤波器的跟踪性能要远优于EKF的性能。     

Virtual target tracking of mobile robot and its application to formation control

A virtual target tracking approach is proposed for kinematic control of mobile robot. In the controller, linear and angular velocity inputs are generated by using the local data of robot position and orientation along with the estimated velocity of target object. Applying the proposed approach to a cooperative robot group with arbitrary number of multiple mobile robots, it is possible to create various robot formations for cooperative navigation and tracking of moving object. The developed controller is shown to be stable and convergent through theoretical proof and a series of experiments.     

Filtering of systems with nonlinear measurements with an application to target tracking

This paper studies the problem of recursive state estimation of stochastic linear systems with nonlinear measurements. The main idea is to rewrite the measurement map in a linear form by considering, as system output, a vector of “virtual” measurements. The result is a linear system with a non‐Gaussian and nonstationary output noise. State estimation is therefore obtained using a Kalman filter or, alternatively, a quadratic filter, suitably designed for non‐Gaussian systems. This work provides two sufficient conditions for the application of the virtual measurement approach and shows its effectiveness in the case of the maneuvering target tracking problem.     

运动目标检测在单兵作战中的应用

考虑到单兵执行长时间侦查和狙击任务时容易出现松懈和疲劳的问题,结合成熟的双目立体视觉测量技术和ViBe算法对运动目标进行检测来进行及时的反馈和提醒。ViBe是像素级别的视频背景建模和前景检测算法,具有效果好、鲁棒性强、速度快和占用硬件资源少的特点。一方面通过ViBe算法对采集到的图像进行运动检测,得到运动目标区域信息;另一方面通过双目摄像机系统进行标定、立体匹配和三维测量,获取运动目标的位置信息。实验结果表明,该系统能够有效地检测到运动目标的区域和位置信息,达到辅助单兵作战的目的。     

Analog circuit for motion detection using a CCD camera based on a biological vision system and its application to mobile robots

We have proposed an analog motion detection circuit based on a biological vision system. A charge coupled device (CCD) camera was used as the input in order to obtain an input image. By using the CCD camera, it is possible to detect the movement of an object as a high-resolution image. Because the unit motion detection circuit is constructed with eight metal oxide semiconductor (MOS) transistors and one capacitor, the structure of the unit circuit is simple. The test circuit was constructed on a circuit board using discrete MOS transistors. The results from the test circuit showed that it can detect the direction of motion. The test circuit was connected to a microcomputer in a mobile robot. It was clear from the results that the motion detection circuit can control a mobile robot.     

Optimal sensor placement and motion coordination for target tracking

This work studies optimal sensor placement and motion coordination strategies for mobile sensor networks. For a target-tracking application with range sensors, we investigate the determinant of the Fisher Information Matrix and compute it in the 2D and 3D cases, characterizing the global minima in the 2D case. We propose motion coordination algorithms that steer the mobile sensor network to an optimal deployment and that are amenable to a decentralized implementation. Finally, our numerical simulations illustrate how the proposed algorithms lead to improved performance of an extended Kalman filter in a target-tracking scenario.     

基于CKF 的分布式滤波算法及其在目标跟踪中的应用

针对已有基于Sigma点信息滤波的分布式滤波算法,其性能易受参数影响而导致应用范围受限的问题,以容积卡尔曼滤波(CKF)为基础,利用信息滤波和平均一致性理论提出一种分布式CKF算法。该算法在保持分布式滤波优良特性(即可扩展性和对节点故障强鲁棒性)的同时,兼具CKF的高滤波精度和强稳定性。仿真结果表明了所提出算法的有效性,与分布式Unscented卡尔曼滤波(UKF)算法相比,该算法显著提高了目标跟踪的精度和稳定性。     

一种基于运动估计的红外目标跟踪方法

为解决红外目标跟踪中目标的交错、遮挡等问题,提出了一种新的基于运动估计的目标跟踪方法。建立目标的方向梯度-灰度直方图特征模型,该模型能较准确地刻画目标特征。使用最大后验概率指标在搜索区域进行目标匹配,该指标能很好地突出目标、抑制背景,并容易得到全局最优解。提出一种新的运动估计方法,即轨迹预测算法,对目标的运动进行较准确的估计。实验结果证明,该方法不仅计算复杂度低,而且能够较好地解决目标交错、遮挡等问题。     

Output feedback tracking control of stochastic Lagrangian systems and its application

This paper focuses on the problem of output feedback tracking control for stochastic Lagrangian systems with the unmeasurable velocity. Under some milder assumptions, using the structural properties of Lagrangian systems, a reduced-order observer is skillfully constructed to estimate the velocity. Based on the observer, an output feedback tracking controller is designed such that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The efficiency of the controller is demonstrated by a stochastic mechanical model.     

An improved target tracking algorithm and its application in intelligent video surveillance system

Multimedia Tools and Applications - Target tracking is one of the pivotal technologies in intelligent video surveillance systems. Facing the complex and various scenarios in practical applications,...     

复杂运动状态的视频目标跟踪自适应切换算法

针对实际环境中运动目标的状态转移模型以及随机噪声分布存在的不确定性,提出了一种适用于复杂运动状态的视频目标跟踪算法。该算法同时结合了Kalman滤波(KF)实时性好的优点,以及粒子滤波(PF)能同时处理非线性、非高斯滤波问题的优点,通过对Kalman滤波性能进行分析,定义了评价滤波性能优劣的参数并作为判断条件,实现了不同运动状态下Kalman滤波和粒子滤波自适应切换。通过实验表明该方法在目标运动状态发生显著变化时仍能够实现稳定跟踪,同时具有较高的跟踪精度。     

奇异时滞系统的新稳定判据及其在部分元件等效电路中的应用

针对奇异时滞系统的稳定性分析问题,利用时滞分解方法,通过引入积分不等式,得出新的时滞相关稳定判据．时滞分解方法的基本思想是：将整个时滞区间等分为多个子区间,在每个子区问上定义不同的能量函数,以此构造新的Lyapunov-Krasovskii泛函．部分元件等效电路用于验证本文所提方法具有更小的保守性．     

Multi-robot target detection and tracking: taxonomy and survey

Target detection and tracking encompasses a variety of decisional problems such as coverage, surveillance, search, patrolling, observing and pursuit-evasion along with others. These problems are studied by several communities, that tackle them using diverse formulations, hypotheses and approaches. This variety and the fact that target related robotics problems are pertinent for a large spectrum of applications has motivated a large amount of contributions, which have mostly been surveyed according to one or another viewpoint. In this article, our objective is to go beyond the frontiers of specific communities and specific problems, and to enlarge the scope of prior surveys. We define classes of missions and problems, and relate the results from various communities according to a unifying taxonomy. We review various work related to each class of problems identified in the taxonomy, highlighting the different approaches, models and results. Finally, we propose a transverse synthesis which analyses the approaches, models and lacks that are recurrent through all the tackled problems, and isolate the current main research directions.     

Control of nonlinear systems with output tracking error constraints and its application to magnetic bearings

A constructive method is presented to design controllers that force the output of nonlinear systems in a strict feedback form to track a bounded and sufficiently smooth reference trajectory asymptotically. Under a suitable condition on the initial output tracking error, the proposed controllers guarantee the output tracking error within a symmetric or an asymmetric pre-specified limit range, and boundedness of all signals of the closed-loop system. A transformation is introduced to take care of the output tracking error constraint. Smooth and/or p-times differentiable step functions are proposed and incorporated in the output tracking error transformation to overcome difficulties due to the asymmetric limit range on the output tracking error. As a result, there are no switchings in the proposed controllers despite the asymmetric limit range. The proposed control design is then applied to design a tracking controller for active magnetic bearings as an illustrating application.     

基于自适应粒子群优化的新型粒子滤波在目标跟踪中的应用

针对基于粒子群优化的粒子滤波(PSO-PF)算法精度不高,实时性差,难以满足雷达机动目标跟踪的需求,提出一种基于动态邻域自适应粒子群优化的粒子滤波(DPSO-PF)算法.该算法可以动态调整粒子邻域环境,其中每个粒子按照邻域的环境和自身的位置信息自适应地调整相互间的邻域粒子数量,使邻域粒子数量更为合理,达到寻优能力与收敛速度的最佳平衡.最后利用不同模型对该算法进行了仿真实验,实验结果表明所提出的算法能够提高雷达机动目标跟踪的实时性和精确性.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

Manoeuvring target tracking with coordinated-turn motion using stochastic non-linear filter

Allowing for perturbations in speed and turn rate of a target moving in a coordinated turn obeys a non-linear stochastic differential equation. Existing algorithms for coordinated turn tracking avoid this problem by ignoring perturbations in the continuous time model and adding process noise only after discretisation. The dynamic model used here adds small perturbations, modelled as independent Brownian motion processes, to the speed and turn rate. The target state is to be recursively estimated from noisy discrete-time measurements of the target's range and bearing. In particular, this paper examines the effect of the perturbations in speed and turn rate on the coordinated turn motion of the aircraft, and subsequently the stochastic algorithm is developed by deriving the evolutions of conditional means and variances for estimating the state of the aircraft. By linearizing the stochastic differential equations about the mean of the state vector using first-order approximation, the mean trajectory of the resulting first-order approximated stochastic differential model does not preserve the perturbation effect felt by the moving target; only the variance trajectory includes the perturbation effect. For this reason, the effectiveness of the perturbed model is examined on the basis of the second-order approximations of the system non-linearity. The theory of the non-linear filter of this paper is developed using the Kolmogorov forward equation ‘between the observation’ and a functional difference equation for the conditional probability density ‘at the observation’. The effectiveness of the second-order non-linear filter is examined on the basis of its ability to preserve perturbation effect felt by the aircraft. The Kolmogorov forward equation, however, is not appropriate for numerical simulations, since it is the equation for the evolution of the conditional probability density. Instead of the Kolmogorov equation, one derives the evolutions for the moments of the state vector, which in our case consists of positions, velocities and turn rate of the manoeuvring aircraft. Even these equations are not appropriate for the numerical simulations, since they are not closed in the sense that computing the evolution of a given moment involves the knowledge of higher-order moments. Hence we consider the approximations to these moment evolution equations. Simulation results are introduced to demonstrate the usefulness of an analytic theory developed in this paper.     

Improving target detection by coupling it with tracking

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where target detection is integrated with tracking to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other, we feed the results of tracking back to the detection stage in order to adaptively optimize the detection threshold and improve system robustness. First, the initial target locations are extracted using background subtraction. To model the background, we employ Support Vector Regression (SVR) which is updated over time using an on-line learning scheme. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and improve the confidence level of detection. For verification, additional information based on size, color and motion information is utilized. Feeding back the results of tracking to the detection stage restricts the range of detection threshold values, suppresses false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing perspective projection distortions. We have validated the proposed framework in two different application scenarios, one detecting vehicles at a traffic intersection using visible video and the other detecting pedestrians at a university campus walkway using thermal video. Our experimental results and comparisons with frame-based detection and kernel-based tracking methods illustrate the robustness of our approach.

一种模糊算法及其在多假设多目标跟踪中的应用

研究了基于模糊交互式多模型算法解决多假设多目标跟踪中的新目标数据相关问题。使用模糊交互式多模型算法处理新目标的数据相关问题,能够提高跟踪精度、时间效率。用隶属度表示每个模型与所跟踪新目标的隶属关系,根据隶属度选择最佳的模型对新目标进行跟踪。仿真结果表明,该方法不仅能够处理新目标的数据相关,同时又能够提高算法的时间效率。