Real-time Target Tracking Research Articles

Active Multimodal Sensor System for Target Recognition and Tracking.

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system.

Tracking a Non-Cooperative Target Using Real-Time Stereovision-Based Control: An Experimental Study.

Tracking a non-cooperative target is a challenge, because in unfamiliar environments most targets are unknown and unspecified. Stereovision is suited to deal with this issue, because it allows to passively scan large areas and estimate the relative position, velocity and shape of objects. This research is an experimental effort aimed at developing, implementing and evaluating a real-time non-cooperative target tracking methods using stereovision measurements only. A computer-vision feature detection and matching algorithm was developed in order to identify and locate the target in the captured images. Three different filters were designed for estimating the relative position and velocity, and their performance was compared. A line-of-sight control algorithm was used for the purpose of keeping the target within the field-of-view. Extensive analytical and numerical investigations were conducted on the multi-view stereo projection equations and their solutions, which were used to initialize the different filters. This research shows, using an experimental and numerical evaluation, the benefits of using the unscented Kalman filter and the total least squares technique in the stereovision-based tracking problem. These findings offer a general and more accurate method for solving the static and dynamic stereovision triangulation problems and the concomitant line-of-sight control.

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Real-time target tracking of soft tissues in 3D ultrasound images based on robust visual information and mechanical simulation.

In this paper, we present a real-time approach that allows tracking deformable structures in 3D ultrasound sequences. Our method consists in obtaining the target displacements by combining robust dense motion estimation and mechanical model simulation. We perform evaluation of our method through simulated data, phantom data, and real-data. Results demonstrate that this novel approach has the advantage of providing correct motion estimation regarding different ultrasound shortcomings including speckle noise, large shadows and ultrasound gain variation. Furthermore, we show the good performance of our method with respect to state-of-the-art techniques by testing on the 3D databases provided by MICCAI CLUST'14 and CLUST'15 challenges.

Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking

Tracking vehicles through dense environments is an important and challenging task that is mostly tackled using visible and near IR wavelengths. Hyperspectral imaging is known to improve the robustness of target identification, but the massive increase in data created is usually prohibitive for tracking many targets. We present a persistent real-time aerial target tracking system, taking advantage of an adaptive, multimodal sensor concept and blending the hyperspectral likelihoods with kinematic likelihoods in a multidimensional assignment framework. The adaptive sensor is capable of providing wide field of view panchromatic images as well as the spectra of small number of pixels. The proposed system does not require large amount of hyperspectral data collection as we focus on tracking fewer number of targets with higher persistency. This overcomes the data challenge of hyperspectral tracking by following dynamic data-driven application systems (DDDAS) principles to control hyperspectral data collection where most beneficial. The DDDAS framework for controlling hyperspectral data collection is developed by incorporating prior information from the filter movement predictions and information from motion detection. The proposed multidimensional hyperspectral feature-aided tracker is compared to a 2-D hyperspectral feature-aided tracker and another cascaded hyperspectral data based tracker by generating a synthetic, realistic, aerial video on a dense scene.

Real‐time multi‐class moving target tracking and recognition

The existing tracking and recognition methods concentrate mainly on single-class targets; however, systems for traffic management or intelligent transport often require multi-class target tracking and recognition in real time. This study proposes an effective multi-class moving target recognition method that is based on Gaussian mixture part-based model, which accurately locates objects of interest and recognises their corresponding categories. The method is multi-threaded and combines soft clustering approach with multiple mixture part based models to provide stable multi-class target tracking and recognition in video sequences. The highlight of the method is its ability to recognise multi-class moving targets and to count their numbers in the video sequence captured by a stationary camera with fixed focal length. Another contribution of this study is that an extended part based model is developed for object recognition in real-world environments, which can improve the overall system performance, lower time costs, and better meet the actual demand of a video system. Experimental results show that the proposed method is viable in real-time multi-class moving target tracking and recognition.

Extended kernelised correlation filter tracking

Due to the impressive high-speed, correlation filters began to receive more attention. In this Letter, a real time target tracking framework based on the kernelised correlation filter is proposed. The framework includes two parts: a boosted fusion feature colour names-histograms of oriented gradients (CN-HOG) that consists of CNs and HOG, and a novel fragments-based tracking algorithm that is robust to partial occlusion. Experiments with the evaluation of state-of-the-art single object trackers show the effectiveness and speed of the proposed method.

Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment

We present a vision system design for landing an unmanned aerial vehicle on a ship's flight deck autonomously. The edge information from the international landing target is used to perform line seg...

Real-time Target Determination and Tracking with An Airborne Video System

This paper investigates real-time target determination, recognition and tracking with an airborne video system. The visual navigation and target tracking in an unmanned aerial vehicle (UAV) are of great interest among scientists. To achieve real-time target determination and tracking, we have to accomplish two missions: to determine the ＂feature＂ of the target in real-time, and to update the ＂feature＂. This paper develops an algorithm to define the feature of the target in real time. With experiment data, we also demonstrate the validity of our algorithm. Our algorithm is potentially applicable to airborne video tracking system in a UAV.

Real-Time and Robust Compressive Background Subtraction for Embedded Camera Networks

Real-time target tracking is an important service provided by embedded camera networks. The first step in target tracking is to extract the moving targets from the video frames, which can be realised by using background subtraction. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computationally efficient. We propose a baseline version which uses luminance only and then extend it to use colour information. The key idea is to use random projection matrics to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, to show the computational efficiency of our methods is not platform specific, we implement it on various platforms. The real implementation shows that our proposed method is consistently better and is up to six times faster, and consume significantly less resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

结合多特征融合算法的人数统计与分析系统

为了实现高准确率的实时人数统计，设计了嵌入式片上系统平台。该系统采用交叉编译的方式，对人体目标进行实时检测与跟踪，实现了对指定区域内的人数统计与分析。本系统利用了AdaBoost分类器对图像/视频中目标进行人脸识别，基于Haar特征提取进行目标跟踪。提出结合多特征融合算法以及人脸识别参数自适应算法，在Xilinx公司的Zynq-7000开发板上实现了目标实时性和快速性的统计与分析。结果表明：该系统充分节省了嵌入式平台的资源，简化了整个系统的开发流程，并提高了系统的兼容性和可移植性。结合多特征的融合算法使人数统计的准确率高达97%以上。该系统具有安装位置灵活，实时性好、准确率高、稳定性好等特点。 In order to achieve high accuracy people counting, the embedded platform is designed in this pa-per. The system realizes real-time target detection and tracking using the cross compiler, to achieve the number of people within the designated area. The system uses the AdaBoost classifier image/video target recognition and feature extraction based on Haar for target tracking. The combination of multi-feature fusion algorithm and face recognition parameters adaptive algorithms is proposed in Xilinx’s Zynq-7000 development board to achieve the goal of real-time and fast statistics and analysis. The results show that: The system saves resources of embedded platforms and simplifies the entire system development process, improving the whole compatibility and portability. It can realize people counting and analysis in the image/video with accuracy rate of more than 97%. Basically it meets the requirements of human counting in traffic statistics: Parallel processing, high accuracy, stable and reliable, etc.

An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise

In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target tracking. The algorithm adopts Gaussian mixture density function to approximately estimate the observation noise. The observation models based on Mini RadaScan for target tracking on offing are introduced, and the observation noise is modelled as glint noise. The Gaussian components are predicted and updated using CKF. A cost function is designed by integral square difference to update the weight of Gaussian components on the time update stage. Based on comparison experiments of constant angular velocity model and maneuver model with different algorithms, the proposed algorithm has the advantages of fast tracking response and high estimation precision, and the computation time should satisfy real-time target tracking requirements.

Concepts And Evolution Of Research In The Field Of Wireless Sensor Networks

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field.

Mobile Target Tracking Based on Hybrid Open-Loop Monocular Vision Motion Control Strategy

This paper proposes a new real-time target tracking method based on the open-loop monocular vision motion control. It uses the particle filter technique to predict the moving target’s position in an image. Due to the properties of the particle filter, the method can effectively master the motion behaviors of the linear and nonlinear. In addition, the method uses the simple mathematical operation to transfer the image information in the mobile target to its real coordinate information. Therefore, it requires few operating resources. Moreover, the method adopts the monocular vision approach, which is a single camera, to achieve its objective by using few hardware resources. Firstly, the method evaluates the next time’s position and size of the target in an image. Later, the real position of the objective corresponding to the obtained information is predicted. At last, the mobile robot should be controlled in the center of the camera’s vision. The paper conducts the tracking test to the L-type and the S-type and compares with the Kalman filtering method. The experimental results show that the method achieves a better tracking effect in the L-shape experiment, and its effect is superior to the Kalman filter technique in the L-type or S-type tracking experiment.

A Phase II Trial of Stereotactic Ablative Body Radiotherapy for Low-Risk Prostate Cancer Using a Non-Robotic Linear Accelerator and Real-Time Target Tracking: Report of Toxicity, Quality of Life, and Disease Control Outcomes with 5-Year Minimum Follow-Up.

Purpose/Objective(s): Herein, we report the results of an IRB-approved phase II trial of Varian Trilogy/TrueBeam-based stereotactic ablative body radiotherapy (SABR) monotherapy for low-risk prostate cancer using the Calypso® System to provide real-time electromagnetic tracking of the prostate’s position during treatment delivery.Materials/Methods: A total of 102 low-risk patients completed protocol treatment between January 2007 and May 2009. A total dose of 40.0 Gy in 5 every-other-day fractions of 8.0 Gy was prescribed to the planning target volume. Target setup and tracking procedures were as follows: (1) the Calypso® System was used to achieve target setup prior to each fraction; (2) conebeam CT imaging was then used for correction of setup error and for assessment of target and organs-at-risk deformations; (3) after treatment delivery was initiated, the Calypso® System then provided real-time intrafractional target tracking. The NCI CTCAE v3.0 was used to assess urinary and rectal toxicity during treatment and at defined follow-up time points. Biochemical response and quality of life measurements were made at concurrent follow-up points.Results: Urinary toxicities were most common. At 6 months, 19.6, 2.9, and 4.9% of patients reported grades 1–2 urinary frequency, dysuria, and retention, respectively. Rectal toxicities were uncommon. By 12 months, 2.9% of patients reported painless rectal bleeding with subsequent symptom resolution without requiring invasive interventions. Quality of life measurements demonstrated a significant decline over baseline in urinary irritative/obstructive scores at 1 month following SABR but otherwise did not demonstrate any difference for bowel, bladder, and sexual function scores at any other follow-up time point. One patient suffered biochemical recurrence at 6 years following SABR.Conclusion: At 5 years, minimum follow-up for this favorable patient cohort, prostate SABR resulted in favorable toxicity, quality of life, and biochemical outcomes.

The visual tracking system using a stereo vision robot

Purpose – The purpose of this paper is to use vision stereo to simultaneously acquire image pairs under a normal environment. Then the methods of moving edges detection and moving target shifting are applied to reduce noise error in order to position a target efficiently. The target is then double confirmed via image merge and alignment. After positioning, the visual difference between the target and the image created by the stereo vision system is measured for alignment. Finally, the image depth of the target is calculated followed by real-time target tracking. Design/methodology/approach – This study mainly applies Sobel image principle. In addition, moving edges detection and moving target shifting are also used to work with system multi-threading for improving image identification efficiency. Findings – The results of the experiment suggest that real-time image tracking and positioning under a pre-set environment can be effectively improved. On the other hand, tracking and positioning are slightly affected under a normal environment. Errors of distance measurements occur because there is more noise existing. Research limitations/implications – This study mainly determines the movements and positioning of an object or a target via image. However, the stability of moving edges detection executed by the stereo vision system can be affected if the light sources in an environment are too strong or extreme. Practical implications – So far the method of tracking and positioning a moving object has been applied to surveillance systems or the application which requires measuring and positioning under a normal environment. The method proposed by this study can also be used to construct a 3D environment. Originality/value – The method proposed by this study can also be used to construct a 3D environment or tracking moving object to measure the distance.

Image Blurring Estimation and Calibration with a Joint Transform Correlator

The Joint Transform Correlator (JTC) has been the most suitable technique for real time optical pattern recognition and target tracking applications. This paper proposes a new application of the JTC system for an analysis of the blurring effect of the optical images caused by a defocused lens. We present the relation between the correlation peak, optical transfer function (OTF), and the amount of blurring caused by focusing error. Moreover, we show a possibility of calibrating the blurred image by simply measuring the correlation peak.

Real-time infrared target tracking based on ℓ1 minimization and compressive features.

Tracking a target in infrared (IR) sequences is a challenging task because of low resolution, low signal-to-noise ratios, occlusion, and poor target visibility. For many civil and military applications, the realtime requirement is always a key factor for tracking algorithms to be used. This undoubtedly makes tracking in IR sequences more difficult. This paper presents a real-time IR target tracking under complex conditions based on l1 minimization and compressive features. First, we adopt a sparse measurement matrix to project the high-dimensional Harr-like features to low-dimensional features that are applied to the appearance modeling. This appearance model allows significant reduction in the computational cost of the target-tracking phase. Then, the appearance model is introduced into the framework of the popular l1 tracker. Each IR target candidate is represented by the appearance template based on the structure of sparse representation. Finally, the candidate that has the minimum reconstruction error is selected as the tracking result. The proposed tracking method can combine the real-time advantages of the compressive tracking and the robustness of the l1 tracker. Experimental results on challenging IR image sequences including both aerial targets and ground targets show that the proposed algorithm has better robustness and real-time performance in comparison with two state-of-the-art tracking algorithms.

Multiple Target Tracking with RF Sensor Networks

RF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We apply radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We introduce methods, inspired by machine vision and adapted to the peculiarities of RTI, that enable accurate and real-time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to four targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets.

Design of the Target Tracking Process Based on DM648

Compared with the traditional vehicle detector, the vehicle detection and tracking based on video image processing and the technique of visual target has fast processing speed, and convenient installation and maintenance, and low cost, wide range of monitoring, can obtain more kinds of traffic parameters, and many other advantages, has become more and more widely used in intelligent transportation system (ITS) in recent years. This paper introduces a method for real-time detection, target tracking in traffic image sequences from a fixed single camera. The System adopts TMS320DM648 as the core processor to implement the real-time target tracking algorithms, mainly complete the effective information real-time display of the software and hardware design of target tracking system, application flexibility, small volume, stable and reliable, it is very practical in practice.