Tracking the soccer ball using multiple fixed cameras

This paper demonstrates innovative techniques for estimating the trajectory of a soccer ball from multiple fixed cameras. Since the ball is nearly always moving and frequently occluded, its size and shape appearance varies over time and between cameras. Knowledge about the soccer domain is utilized and expressed in terms of field, object and motion models to distinguish the ball from other movements in the tracking and matching processes. Using ground plane velocity, longevity, normalized size and color features, each of the tracks obtained from a Kalman filter is assigned with a likelihood measure that represents the ball. This measure is further refined by reasoning through occlusions and back-tracking in the track history. This can be demonstrated to improve the accuracy and continuity of the results. Finally, a simple 3D trajectory model is presented, and the estimated 3D ball positions are fed back to constrain the 2D processing for more efficient and robust detection and tracking. Experimental results with quantitative evaluations from several long sequences are reported.     

一种鲁棒的时空上下文快速跟踪算法

为解决时空上下文快速跟踪算法在目标处于复杂背景及被遮挡情况下容易产生漂移的问题,提出了一种鲁棒的时空上下文快速跟踪算法,通过引入Kalman滤波器,对当前帧中的目标在下一帧中的位置进行估计和预测,并将其作为下一帧时空上下文快速跟踪算法的迭代起点。对不同视频序列的跟踪结果表明,与时空上下文快速跟踪算法和多示例学习跟踪算法相比,提出的算法在目标被遮挡及复杂背景情况下能够更准确地跟踪到目标,并且满足实时性要求。     

时空上下文抗遮挡视觉跟踪

目的 传统的视觉跟踪方法只考虑了目标本身的特征信息提取,忽略了目标周围稠密的上下文信息。一旦目标本身特征信息提取存在困难,很容易导致跟踪失败。为解决上述问题,提出一种时空上下文抗遮挡视觉跟踪算法（STC-PF）。方法 首先,利用目标与局部区域的时空关系学习时空上下文模型;然后,通过上下文先验模型和学习到的时空上下文模型计算置信图;最后,对时空上下文区域进行分块遮挡判别,若遮挡概率小于设定阈值,计算置信图所得最大概率位置即为目标位置;若遮挡概率大于设定阈值,则目标发生遮挡,通过子块匹配和粒子滤波估计目标位置以及运动轨迹,实现不同程度的抗遮挡跟踪。结果 对测试数据集中的图像序列进行实验,结果表明,STC-PF方法的跟踪成功率提高至80%以上;中心误差小于原算法;同时STC-PF算法在提高抗遮挡能力的前提下,运行速度与原算法相当,高于当前流行算法。结论 STC-PF算法能够适用于光照变化、目标旋转、遮挡等复杂情况下的视觉目标跟踪,具有一定的实时性和高效性,尤其是在目标发生遮挡情况下具有很好的抗遮挡能力和较快的运行速度。     

Tracking multiple features using relaxation

A new algorithm is introduced for tracking multiple features in an image sequence. First, the proposed method iteratively reduces the disparity of each possible match by relaxation labeling. It is assumed that all trajectories are smooth and the smoothness is used as the measure for correspondence. Some cases of wrong correspondences can be recovered by a proposed scheme called constraint-aided exchange during the tracking process. Occluded or missing feature points can be detected and predicted in the proposed algorithm. Finally, the algorithm is applied to data obtained from real world scenes. The human motion analysis can be achieved by the tracking algorithm.     

Robust particle tracking via spatio-temporal context learning and multi-task joint local sparse representation

Multimedia Tools and Applications - Particle filters have been proven very successful for non-linear and non-Gaussian estimation problems and extensively used in object tracking. However, high...     

Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes

Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.     

Vehicle re-identification using multi-task deep learning network and spatio-temporal model

Multimedia Tools and Applications - Vehicle re-identification (re-ID) plays an important role in the automatic analysis of the increasing urban surveillance videos and has become a hot topic in...     

Behavior monitoring for assistive environments using multiple views

This work presents an approach to behavior understanding using multiple cameras. This approach is appropriate for monitoring people in an assistive environment for the purpose of issuing alerts in cases of abnormal behavior. The output of multiple classifiers is used to model and extract abnormal behavior from both the target trajectory and the target short-term activity (i.e., walking, running, abrupt motion, etc.). Spatial information is obtained after an offline camera registration using homography information. The proposed approach is verified experimentally in an indoor environment. The experiments are performed with a single moving target; however, the method can be generalized to multiple moving targets, which may occlude each other, due to the use of multiple cameras.     

Tracking multiple objects using the Condensation algorithm

Some years ago a new tracker, the Condensation algorithm, came to be known in the computer vision community. It describes a stochastic approach that has neither restrictions on the system and measurement models used nor on the distributions of the error sources, but it cannot track an arbitrary changing number of objects. In this paper an extension of the Condensation algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can flow directly into the tracking process, such that newly appearing objects can be handled.     

遮挡条件下多运动车辆跟踪研究

针对SURF算法能够提取到的图像特征点较少的问题,基于保持亮度特性的双直方图均衡算法,通过重构SURF尺度空间提取图像特征。将这种方法与卡尔曼滤波相结合进行目标跟踪,用特征点的中心作为跟踪点;通过卡尔曼滤波预测出运动目标的位置,判断遮挡是否发生;最后,应用该方法进行目标特征向量匹配。实验结果表明,该算法对发生旋转、缩放以及遮挡的多运动目标都可进行稳定跟踪,其跟踪速度比R-SURF算法提高20%;在跟踪速度相当的情况下,跟踪精度要高于卡尔曼滤波跟踪算法。     

Co-metric: a metric learning algorithm for data with multiple views

We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In this paper, motivated by the co-training framework, we propose an algorithm-independent framework, named co-metric, to learn Mahalanobis metrics in multi-view settings. In its implementation, an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples. Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop. This procedure is repeated until some stop criteria are met. The framework can accommodate most existing metric learning algorithms whether types-of-side-information or example-labels are used. In addition it can naturally deal with semi-supervised circumstances under more than two views. Our comparative experiments demonstrate its competiveness and effectiveness.     

增量学习Rao-Blackwellized粒子滤波的跟踪算法

基于特征子空间的目标跟踪方法能适应目标状态的变化,并对光照等外部环境不敏感,但通常假定特征子空间的基向量固定,这样不仅需要离线训练,而且在目标姿态发生较大改变时,跟踪精度会降低。提出一种基于增量学习的Rao-Blackwellized粒子滤波算法,通过在线学习获得特征子空间的基向量,并用解析的方法对目标在子空间的投影参数进行在线更新。实验表明,新算法在目标有较大形变、姿态变化和光照等条件变化时,能保持较高跟踪精度,具有较强的鲁棒性。     

Tracking people in video sequences using multiple models

This paper presents a multiple model real-time tracking technique for video sequences, based on the mean-shift algorithm. The proposed approach incorporates spatial information from several connected regions into the histogram-based representation model of the target, and enables multiple models to be used to represent the same object. The use of several regions to capture the color spatial information into a single combined model, allow us to increase the object tracking efficiency. By using multiple models, we can make the tracking scheme more robust in order to work with sequences with illumination and pose changes. We define a model selection function that takes into account both the similarity of the model with the information present in the image, and the target dynamics. In the tracking experiments presented, our method successfully coped with lighting changes, occlusion, and clutter.     

People counting via multiple views using a fast information fusion approach

Real-time estimates of a crowd size is a central task in civilian surveillance. In this paper we present a novel system counting people in a crowd scene with overlapping cameras. This system fuses all single view foreground information to localize each person present on the scene. The purpose of our fusion strategy is to use the foreground pixels of each single views to improve real-time objects association between each camera of the network. The foreground pixels are obtained by using an algorithm based on codebook. In this work, we aggregate the resulting silhouettes over cameras network, and compute a planar homography projection of each camera’s visual hull into ground plane. The visual hull is obtained by finding the convex hull of the foreground pixels. After the projection into the ground plane, we fuse the obtained polygons by using the geometric properties of the scene and on the quality of each camera detection. We also suggest a region-based approach tracking strategy which keeps track of people movements and of their identities along time, also enabling tolerance to occasional misdetections. This tracking strategy is implemented on the result of the views fusion and allows to estimate the crowd size dependently on each frame. Assessment of experiments using public datasets proposed for the evaluation of counting people system demonstrates the performance of our fusion approach. These results prove that the fusion strategy can run in real-time and is efficient for making data association. We also prove that the combination of our fusion approach and the proposed tracking improve the people counting.

     

Providing synthetic views for teleoperation using visual posetracking in multiple cameras

This paper describes a visual tool for teleoperative experimentation involving remote manipulation and contact tasks. Using modest hardware, it recovers in real time the pose of moving polyhedral objects, and presents a synthetic view of the scene to the operator of a teleoperated robot using any chosen viewpoint and viewing direction. To recover pose, the method of line tracking first introduced by Harris (1992) is extended to multiple calibrated cameras, and its dynamic performance improved using robust methods and iterative filtering. Experiments are reported which determine the static and dynamic performance of the vision system, and its use in teleoperation is illustrated in two experiments, a peg-in-hole manipulation task and an impact control task     

Automatic 3D object segmentation in multiple views using volumetric graph-cuts

We propose an algorithm for automatically obtaining a segmentation of a rigid object in a sequence of images that are calibrated for camera pose and intrinsic parameters. Until recently, the best segmentation results have been obtained by interactive methods that require manual labelling of image regions. Our method requires no user input but instead relies on the camera fixating on the object of interest during the sequence. We begin by learning a model of the object’s colour, from the image pixels around the fixation points. We then extract image edges and combine these with the object colour information in a volumetric binary MRF model. The globally optimal segmentation of 3D space is obtained by a graph-cut optimisation. From this segmentation an improved colour model is extracted and the whole process is iterated until convergence.