基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

使用计算机视觉的3D模型动作记录器

该文旨在完成一款基于计算机视觉的3D模型动作记录器,即计算机通过摄像头获取人体运动视频并检测跟踪,之后通过处理数据控制3D模型,从而将人体动作进行记录保存。文章主要围绕运动目标检测、运动目标跟踪和3D建模三个方面展开研究。运动目标检测方面使用OpenCV（Open Source Computer Vision Library）提供的背景差分算法对目标进行分析并提取差分元素;运动目标跟踪方面则研究了常用的Camshift跟踪算法,实现对运动目标的连续跟踪以及识别从而保证动作记录器的连贯性;3D建模部分则使用3Dmax进行建立模型以及骨骼动画的制作处理,并使用Ogremax导出模型;而模型的骨骼动画则由OGRE导入测试环境并根据之前的处理结果进行相应的控制,从而实现人体运动的动作记录。     

Online Approach for Spatio-Temporal Trajectory Data Reduction for Portable Devices

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but effcient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.     

基于水平集的多运动目标时空分割与跟踪

针对背景运动时的运动目标分割问题,提出了一种对视频序列中的多个运动目标进行分割和跟踪的新方法。该方法着眼于运动的且较为复杂的背景,首先利用光流约束方程和背景运动模型建立一个基于时空域的能量函数,然后用该函数进行背景运动速度的估算和运动目标的分割和跟踪。而时空域中的运动目标的最佳分割,乃是通过使该能量函数最小化来驱动时空曲面演化实现。时空曲面的演化采用了水平集PDEs(Partial Differential Equations)方法。实验中,用实际的图像序列验证了该算法及其数值实现。实验表明,该方法能够同时进行背景运动速度的估算、运动目标的分割和跟踪。     

Generating 3D architectural models based on hand motion and gesture

This paper presents a novel method for rapidly generating 3D architectural models based on hand motion and design gestures captured by a motion capture system. A set of sign language-based gestures, architectural hand signs (AHS), has been developed. AHS is performed on the left hand to define various “components of architecture”, while “location, size and shape” information is defined by the motion of Marker-Pen on the right hand. The hand gestures and motions are recognized by the system and then transferred into 3D curves and surfaces correspondingly. This paper demonstrates the hand gesture-aided architectural modeling method with some case studies.     

Navigation system including associative memory

A new adaptive linear robot control system for a robot work cell that can visually track and intercept stationary and moving objects undergoing arbitrary motion anywhere along its predicted trajectory within the robot's workspace is presented in this paper. The proposed system was designed by integrating a stationary monocular CCD camera with off-the-shelf frame grabber and an industrial robot operation into a single application on the MATLAB platform. A combination of the model based object recognition technique and a learning vector quantization network is used for classifying stationary objects without overlapping. The optical flow technique and the MADALINE network are used for determining the target trajectory and generating the predicted robot trajectory based on visual servoing, respectively. The necessity of determining a model of the robot, camera, all the stationary and moving objects, and environment is eliminated. The location and image features of these objects need not be preprogrammed, marked and known before, and any change in a task is possible without changing the robot program. After the learning process on the robot, it is shown that the KUKA robot is capable of tracking and intercepting both stationary and moving objects at an optimal rendezvous point on the conveyor accurately in real-time.     

Motion synchronization of dual-cylinder pneumatic servo systems with integration of adaptive robust control and cross-coupling approach

We investigate motion synchronization of dual-cylinder pneumatic servo systems and develop an adaptive robust synchronization controller. The proposed controller incorporates the cross-coupling technology into the integrated direct/indirect adaptive robust control （DIARC） architecture by feeding back the coupled position errors, which are formed by the trajectory tracking errors of two cylinders and the synchronization error between them. The controller employs an online recursive least squares estimation algorithm to obtain accurate estimates of model parameters for reducing the extent of parametric uncertainties, and uses a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. Therefore, asymptotic convergence to zero of both trajectory tracking and synchronization errors can be guaranteed. Experimental results verify the effectiveness of the proposed controller.     

A distributed device diagnostics system utilizing augmented reality and 3D audio

Augmented reality (AR), combining virtual environments with the perception of the real world, can be used to provide instructions for routine maintenance and error diagnostics of technical devices. The Rockwell Science Center (RSC) is developing a system that utilizes AR techniques to provide “X-ray vision” into real objects. The system can overlay 3D rendered objects, animations, and text annotations onto the video image of a known object. An automated speech recognition system allows the user to query the status of device components. The response is given as an animated rendition of a CAD model and/or as auditory cues using 3D audio. This diagnostics system also allows the user to leave spoken annotations attached to device modules as ASCII text. The position of the user/camera relative to the device is tracked by a computer-vision-based tracking system using fiducial markers. The system is implemented on a distributed network of PCs, utilizing standard commercial off-the-shelf components (COTS).     

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.     

Matching actions in presence of camera motion

When the camera viewing an action is moving, the motion observed in the video not only contains the motion of the actor but also the motion of the camera. At each time instant, in addition to the camera motion, a different view of the action is observed. In this paper, we propose a novel method to perform action recognition in presence of camera motion. Proposed method is based on the epipolar geometry between any two views. However, instead of relating two static views using the standard fundamental matrix, we model the motions of independently moving cameras in the equations governing the epipolar geometry and derive a new relation which is referred to as the “temporal fundamental matrix.” Using the temporal fundamental matrix, a matching score between two actions is computed by evaluating the quality of the recovered geometry. We demonstrate the versatility of the proposed approach for action recognition in a number of challenging sequences.     

Tracking moving objects during low altitude flight

A computer-vision system to assist pilots during low-altitude flight has been developed in this research study. During this critical section of flight, a system that can detect various objects on the ground would be very useful both for enhancing the safety of navigation and for relieving pilots of a part of their workload in flight control. Such tasks can generally be automated by computer-vision-based methods, which provide the ability for object detection and tracking. This paper describes the algorithms developed for accomplishing such tasks. There are two main stages in the vision system. First, independently moving objects in the scene are detected and segmented from the background. Then, they are tracked from frame to frame, and their 3D motion parameters are recursively estimated with Kalman filtering techniques. Experiments using real-world image sequences have been carried out, and the results show that tracking moving objects is successful and the estimation of object's motion parameters are quite accurate.     

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.     

An integrated virtual environment for feasibility studies and implementation of aerial MonoSLAM

This work presents a complete framework of an integrated aerial virtual environment (IAVE), which could effectively help implementing MonoSLAM (single-camera simultaneous localization and mapping) on an aerial vehicle. The developed system allows investigating different flight conditions without using any preloaded maps or predefined features. A 3D graphical engine integrated with a full 6 DOF aircraft dynamic simulator together with its trajectory generator completes the package. The 3D engine generates and accumulates real-time images of a general camera installed on the aerial vehicle. We effectively exploit C++ to develop the 3D graphics engine (3DGE) and all its associated visual effects, including different types of lighting, climate conditions, and moving objects. The existing 3DGE exploits the so-called Frenet Adapted Frames (FAF) with constrained angular velocities that is very effective in motion modeling of both ground and aerial moving objects. An in-house-developed MATLAB GUI puts into service the offline MonoSLAM system, which is very user friendly. The current version of IAVE effectively employs the so-called Inverse Depth Parameterization notions for features?? depth estimation in monocular SLAM, where different case studies show its dependable results for low-cost aerial navigation of a general aviation low-speed aircraft.     

Coupled object detection and tracking from static cameras and moving vehicles

We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in an MDL hypothesis selection framework, which allows it to recover from mismatches and temporarily lost tracks. Building upon a multi-view/multi-category object detector, it localizes cars and pedestrians in the input images. The 2D object detections are converted to 3D observations, which are accumulated in a world coordinate frame. Trajectory analysis in a spacetime window yields physically plausible trajectory candidates. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The resulting approach can initialize automatically and track a large and varying number of objects from both static and moving cameras. We evaluate our approach on several challenging video sequences with both a surveillance-type scenario and a scenario where the input videos are taken from a moving vehicle.     

Tracking and Motion Estimation of the Articulated Object: a Hierarchical Kalman Filter Approach

Real-time motion capture plays a very important role in various applications, such as 3D interface for virtual reality systems, digital puppetry, and real-time character animation. In this paper we challenge the problem of estimating and recognizing the motion of articulated objects using theoptical motion capturetechnique. In addition, we present an effective method to control the articulated human figure in realtime.The heart of this problem is the estimation of 3D motion and posture of an articulated, volumetric object using feature points from a sequence of multiple perspective views. Under some moderate assumptions such as smooth motion and known initial posture, we develop a model-based technique for the recovery of the 3D location and motion of a rigid object using a variation of Kalman filter. The posture of the 3D volumatric model is updated by the 2D image flow of the feature points for all views. Two novel concepts – the hierarchical Kalman filter (KHF) and the adaptive hierarchical structure (AHS) incorporating the kinematic properties of the articulated object – are proposed to extend our formulation for the rigid object to the articulated one. Our formulation also allows us to avoid two classic problems in 3D tracking: the multi-view correspondence problem, and the occlusion problem. By adding more cameras and placing them appropriately, our approach can deal with the motion of the object in a very wide area. Furthermore, multiple objects can be handled by managing multiple AHSs and processing multiple HKFs.We show the validity of our approach using the synthetic data acquired simultaneously from the multiple virtual camera in a virtual environment (VE) and real data derived from a moving light display with walking motion. The results confirm that the model-based algorithm works well on the tracking of multiple rigid objects.     

Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching

Efficient detection and tracking of moving objects in real life conditions is a very challenging research issue, mainly due to occlusions, illumination variations, appearance (disappearance) of new (existing) objects and overlapping issues. In this paper, we address these difficulties by incorporating non-linear and recursive identification mechanisms in motion-based detection and tracking algorithms. Non-linearity allows correct identification of object of complex visual properties while the adaptability makes the proposed scheme able to update its behaviour to the dynamic environmental changes. In addition, in this paper, we introduce the concept of polar spectrum which is a measure for determining the deviation of a vehicle trajectory from an ideal trace. The proposed methods (object tracking and trajectory matching) are applied in survey engineering problems dealing with safe design road turns. In particular, the automatically detected trajectory of a moving vehicle is compared with the ideal trace, through the polar spectrum measure, to determine the safety of a road turn. This trace is also compared with the one manually derived using photogrammetric algorithms and a small error is obtained verifying the efficiency of the method.     

Feature Point Tracking for Incomplete Trajectories

A new algorithm is presented for feature point based motion tracking in long image sequences. Dynamic scenes with multiple, independently moving objects are considered in which feature points may temporarily disappear, enter and leave the view field. This situation is typical for surveillance and scene monitoring applications. Most of the existing approaches to feature point tracking have limited capabilities in handling incomplete trajectories, especially when the number of points and their speeds are large, and trajectory ambiguities are frequent. The proposed algorithm was designed to efficiently resolve these ambiguities. Correspondences between moving points are established in a competitive linking process that develops as the trajectories grow. Appearing and disappearing points are treated in a natural way as the points that do not link. The proposed algorithm compares favorably to efficient alternative algorithms selected and tested in a performance evaluation study. Received: June 8, 1998; revised November 18, 1998     

3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model

Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci^®${\mathrm{daVinci}}^{\circledR }$ surgical robotic platform and a synthesized data set with known ground truth.