Articulated body motion tracking using illumination invariant optical flow

We propose a model-based tracking method for articulated objects in monocular video sequences under varying illumination conditions. The tracking method uses estimates of optical flows constructed by projecting model textures into the camera images and comparing the projected textures with the recorded information. An articulated body is modelled in terms of 3D primitives, each possessing a specified texture on its surface. An important step in model-based tracking of 3D objects is the estimation of the pose of the object during the tracking process. The optimal pose is estimated by minimizing errors between the computed optical flow and the projected 2D velocities of the model textures. This estimation uses a least-squares method with kinematic constraints for the articulated object and a perspective camera model. We test our framework with an articulated robot and show results.     

基于模糊逻辑的多相机非重叠场景的物体匹配

An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.     

Experimenting with nonintrusive motion capture in a virtual environment

A growing number of promising applications requires recognizing human posture and motion. Conventional techniques require us to attach foreign objects to the body, which in some applications is disturbing or even impossible. New, nonintrusive motion capture approaches are called for. The well-known shape-from-silhouette technique for understanding 3D shapes could also be effective for human bodies. We present a novel technique for model-based motion capture that uses silhouettes extracted from multiple views. A 3D reconstruction of the performer can be computed from a silhouette with a technique known as volume intersection. We can recover the posture by fitting a model of the human body to the reconstructed volume. The purpose of this work is to test the effectiveness of this approach in a virtual environment by investigating the precision of the posture and motion obtained with various numbers and arrangements of stationary cameras. An average 1% position error has been obtained with five cameras.     

Multi-object reconstruction from dynamic scenes: An object-centered approach

In this paper, we present a new framework for three-dimensional (3D) reconstruction of multiple rigid objects from dynamic scenes. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In our framework, we aim to reconstruct the 3D models of multiple objects in a more general setting where the configuration of the objects varies among views. We solve this problem by object-centered decomposition of the dynamic scenes using unsupervised co-recognition approach. Unlike conventional motion segmentation algorithms that require small motion assumption between consecutive views, co-recognition method provides reliable accurate correspondences of a same object among unordered and wide-baseline views. In order to segment each object region, we benefit from the 3D sparse points obtained from the structure-from-motion. These points are reliable and serve as automatic seed points for a seeded-segmentation algorithm. Experiments on various real challenging image sequences demonstrate the effectiveness of our approach, especially in the presence of abrupt independent motions of objects.     

Learned Models for Estimation of Rigid and Articulated Human Motion from Stationary or Moving Camera

We propose an approach for modeling, measurement and tracking of rigid and articulated motion as viewed from a stationary or moving camera. We first propose an approach for learning temporal-flow models from exemplar image sequences. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are then incorporated to model the movement of regions of rigid or articulated objects. These spatio-temporal flow models are subsequently used as the basis for simultaneous measurement and tracking of brightness motion in image sequences. Then we address the problem of estimating composite independent object and camera image motions. We employ the spatio-temporal flow models learned through observing typical movements of the object from a stationary camera to decompose image motion into independent object and camera motions. The performance of the algorithms is demonstrated on several long image sequences of rigid and articulated bodies in motion.     

Contour tracking with automatic motion model switching

In this paper we present an efficient contour-tracking algorithm which can track 2D silhouette of objects in extended image sequences. We demonstrate the ability of the tracker by tracking highly deformable contours (such as walking people) captured by a static camera. We represent contours (silhouette) of moving objects by using a cubic B-spline. The tracking algorithm is based on tracking a lower dimensional shape space (as opposed to tracking in spline space). Tracking the lower dimensional space has proved to be fast and efficient. The tracker is also coupled with an automatic motion-model switching algorithm, which makes the tracker robust and reliable when the object of interest is moving with multiple motion. The model-based tracking technique provided is capable of tracking rigid and non-rigid object contours with good tracking accuracy.     

Recovery of nonrigid motion and structure

The authors introduce a physically correct model of elastic nonrigid motion. This model is based on the finite element method, but decouples the degrees of freedom by breaking down object motion into rigid and nonrigid vibration or deformation modes. The result is an accurate representation for both rigid and nonrigid motion that has greatly reduced dimensionality, capturing the intuition that nonrigid motion is normally coherent and not chaotic. Because of the small number of parameters involved, this representation is used to obtain accurate overstrained estimates of both rigid and nonrigid global motion. It is also shown that these estimates can be integrated over time by use of an extended Kalman filter, resulting in stable and accurate estimates of both three-dimensional shape and three-dimensional velocity. The formulation is then extended to include constrained nonrigid motion. Examples of tracking single nonrigid objects and multiple constrained objects are presented     

Multi-scale 2D tracking of articulated objects using hierarchical spring systems

This paper presents a flexible framework to build a target-specific, part-based representation for arbitrary articulated or rigid objects. The aim is to successfully track the target object in 2D, through multiple scales and occlusions. This is realized by employing a hierarchical, iterative optimization process on the proposed representation of structure and appearance. Therefore, each rigid part of an object is described by a hierarchical spring system represented by an attributed graph pyramid. Hierarchical spring systems encode the spatial relationships of the features (attributes of the graph pyramid) describing the parts and enforce them by spring-like behavior during tracking. Articulation points connecting the parts of the object allow to transfer position information from reliable to ambiguous parts. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the hierarchical spring systems.     

Reprint of: Multi-scale 2D tracking of articulated objects using hierarchical spring systems

This paper presents a flexible framework to build a target-specific, part-based representation for arbitrary articulated or rigid objects. The aim is to successfully track the target object in 2D, through multiple scales and occlusions. This is realized by employing a hierarchical, iterative optimization process on the proposed representation of structure and appearance. Therefore, each rigid part of an object is described by a hierarchical spring system represented by an attributed graph pyramid. Hierarchical spring systems encode the spatial relationships of the features (attributes of the graph pyramid) describing the parts and enforce them by spring-like behavior during tracking. Articulation points connecting the parts of the object allow to transfer position information from reliable to ambiguous parts. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the hierarchical spring systems.     

Computing the generalized aspect graph for objects with movingparts

Algorithms for computing the aspect graph representation are generalized to include a larger, more realistic domain of objects known as articulated assemblies those objects composed of rigid parts with articulated connections allowed between parts. The generalization suggests two slightly different representations: one that directly summarizes the possible general views of the object and another (hierarchical) form summarizing the possible general configurations and their respective views. Algorithms are outlined for computing both representations. The generalized aspect graphs of assemblies formed using translational connections are examined     

A mathematical model for computer image tracking

A mathematical model using an operator formulation for a moving object in a sequence of images is presented. Time-varying translation and rotation operators are derived to describe the motion. A variational estimation algorithm is developed to track the dynamic parameters of the operators. The occlusion problem is alleviated by using a predictive Kalman filter to keep the tracking on course during severe occlusion. The tracking algorithm (variational estimation in conjunction with Kalman filter) is implemented to track moving objects with occasional occlusion in computer-simulated binary images.     

Extending 3D Lucas–Kanade tracking with adaptive templates for head pose estimation

The Lucas–Kanade tracker (LKT) is a commonly used method to track target objects over 2D images. The key principle behind the object tracking of an LKT is to warp the object appearance so as to minimize the difference between the warped object’s appearance and a pre-stored template. Accordingly, the 2D pose of the tracked object in terms of translation, rotation, and scaling can be recovered from the warping. To extend the LKT for 3D pose estimation, a model-based 3D LKT assumes a 3D geometric model for the target object in the 3D space and tries to infer the 3D object motion by minimizing the difference between the projected 2D image of the 3D object and the pre-stored 2D image template. In this paper, we propose an extended model-based 3D LKT for estimating 3D head poses by tracking human heads on video sequences. In contrast to the original model-based 3D LKT, which uses a template with each pixel represented by a single intensity value, the proposed model-based 3D LKT exploits an adaptive template with each template pixel modeled by a continuously updated Gaussian distribution during head tracking. This probabilistic template modeling improves the tracker’s ability to handle temporal fluctuation of pixels caused by continuous environmental changes such as varying illumination and dynamic backgrounds. Due to the new probabilistic template modeling, we reformulate the head pose estimation as a maximum likelihood estimation problem, rather than the original difference minimization procedure. Based on the new formulation, an algorithm to estimate the best head pose is derived. The experimental results show that the proposed extended model-based 3D LKT achieves higher accuracy and reliability than the conventional one does. Particularly, the proposed LKT is very effective in handling varying illumination, which cannot be well handled in the original LKT.     

基于视觉的增强现实运动跟踪算法

增强现实系统不仅具有虚拟现实的特点同时具有虚实结合的新特性，为实现虚拟物体与真实物体间的完善结合，必须实时地动态跟踪摄像与真实物体间的相对位置和方向，建立观测模,墼是而通过动态三维显示技术迅速地将虚拟物体添加到真实物体之上，然而目前大多数增强现实系统的注册对象均匀静物体，运动物体的注册跟踪尚很少有人涉足。该算法通过标志点的光流场估计真实环境中运动物体的运动参数，根据透视投影原理和刚体的运动特性确定摄像机与运动物体间的相对位置和方向，实现增强现实系统的运动目标跟踪注册。该算法构架简单、实时性强，易于实现，扩展了增强现实系统的应用范围。     

Object tracking method based on data computing

The data computing process is utilized in various areas such as autonomous driving. Autonomous vehicles are intended to detect and track nearby moving objects avoiding collisions and to navigate in complex situations, such as heavy traffic and dense pedestrian areas. Therefore, object tracking is the core technology in the environment perception systems of autonomous vehicles and requires the monitoring of surrounding objects and the prediction of the moving states of objects in real time. In this paper, a multiple object tracking method based on light detection and ranging (LiDAR) data is proposed by using a Kalman filter and data computing process. We suppose that the movements of the tracking objects are captured consecutively as frames; thus, model-based detection and tracking of dynamic objects are possible. A Kalman filter is applied for predicting posterior state of tracking object based on anterior state of the tracking object. State denotes the positions, shapes, and sizes of objects. By computing the likelihood probability between predicted tracking objects and clusters which registered from tracking objects, the data association process of the tracking objects can be generated. Experimental results showed enhanced object tracking performance in a dynamic environment. The average matching probability of the tracking object was greater than 92.9%.

     

Motion analysis of articulated objects from monocular images

This paper presents a new method of motion analysis of articulated objects from feature point correspondences over monocular perspective images without imposing any constraints on motion. An articulated object is modeled as a kinematic chain consisting of joints and links, and its 3D joint positions are estimated within a scale factor using the connection relationship of two links over two or three images. Then, twists and exponential maps are employed to represent the motion of each link, including the general motion of the base link and the rotation of other links around their joints. Finally, constraints from image point correspondences, which are similar to that of the essential matrix in rigid motion, are developed to estimate the motion. In the algorithm, the characteristic of articulated motion, i.e., motion correlation among links, is applied to decrease the complexity of the problem and improve the robustness. A point pattern matching algorithm for articulated objects is also discussed in this paper. Simulations and experiments on real images show the correctness and efficiency of the algorithms.     

Motion estimation of elastic articulated objects from points and contours with volume invariable constraint

This paper presents a model of elastic articulated objects based on revolving conic surface and a method of model-based motion estimation. The model includes 3D object skeleton and deformable surfaces that can represent the deformation of human body surfaces. In each limb, surface deformation is represented by adjusting one or two deformation parameters. Then, the 3D deformation parameters are determined by corresponding 2D image points and contours with volume invariable constraint from a sequence of stereo images. The 3D motion parameters are estimated based on the 3D model. The algorithm presented in this paper includes model-based parameter estimation of motion and parameter determination of deformable surfaces.     

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depth-colour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.     

Three-dimensional motion computation and object segmentation in a long sequence of stereo frames

We address the problem of computing the three-dimensional motions of objects in a long sequence of stereo frames. Our approach is bottom-up and consists of two levels. The first level deals with the tracking of 3D tokens from frame to frame and the estimation of their kinematics. The processing is completely parallel for each token. The second level groups tokens into objects based on their kinematic parameters, controls the processing at the low level to cope with problems such as occlusion, disappearance, and appearance of tokens, and provides information to other components of the system. We have implemented this approach using 3D line segments obtained from stereo as the tokens. We use classical kinematics and derive closed-form solutions for some special, but useful, cases of motions. The motion computation problem is then formulated as a tracking problem in order to apply the extended Kalman filter. The tracking is performed in a prediction-matching-update loop in which multiple matches can be handled. Tokens are labeled by a number called its support of existence which measures their adequation to the measurements. If this number goes beyond a threshold, the token disappears. The individual line segments can be grouped into rigid objects according to the similarity of their kinematic parameters. Experiments using synthetic and real data have been carried out and the results found to be quite good.     

基于改进运动历史图像的多运动目标实时跟踪

提出了一种利用视频图像对运动目标进行实时检测与跟踪的新方法.该方法利用基于改进的时间片的运动历史图像(tMHI)的灰度阶梯轮廓方法对多个运动目标进行检测,通过卡尔曼滤波器对多目标进行跟踪,并得到了各个运动目标的轨迹曲线,进而实现了对视频图像中多目标的跟踪.同时,该方法对多个目标的遮挡问题获得了明显的改善效果.实验结果表明,该方法能够对复杂场景下的多个目标进行有效的识别和准确的跟踪,系统的实时性强,识别率高,而且该方法对于复杂视频监视系统场景中的光照变化、雨雾等干扰具有较强的稳健性.     

Recovery of 3D animal motions using cameras and mirrors

We present a system that tracks an articulated body performing 3D movement with occlusions using a combination of cameras and mirrors. By integrating cameras and mirrors we get a simultaneous coverage of almost every point on the target and avoid occlusions. The suggested setup is much simpler and easier to handle compared to the equivalent, camera-based setup. Our tracking algorithm is model-based, and errors in the model are treated using the bundle adjustment procedure. In order to deal with the problem of feature visibility, each feature is set to be valid or invalid based on the model and on its expected appearance; this ensures that the system always tracks a set of distinguishable features. The proposed algorithm was able to track targets in 3D using the Gauss–Newton method to minimize geometric errors. We tested our setup by tracking the chameleon’s eyes. Tracking the eyes of a chameleon can be considered as the estimation of the 3D pose of an articulated body, where the head of the chameleon is considered as a rigid body, and each of the two eyes has additional two degrees of freedom. The algorithm proposed can be easily expanded to cope with more complex objects.