多外观模型的鲁棒人脸跟踪

为了解决在真实场景中进行视觉人脸跟踪时不同挑战之间的外观匹配问题,提出了一种多外观模型的人脸跟踪算法。该算法利用多个具有长期和短期外观记忆的外观模型进行有效的人脸跟踪,对变形、旋转、尺度和光照变化表现出鲁棒性。同时利用先检测后跟踪算法的优点,通过使用人脸检测器来处理人脸的剧烈外观变化,检测器也有助于在漂移过程中重新初始化所提算法。最后提出了一种加权分数级融合策略,通过在可能的人脸位置生成的候选人脸来获得融合值最高的人脸跟踪输出。实验结果证明,该跟踪器在自启动时表现出色,性能优于许多先进的跟踪器。     

Robust object tracking via deformation samples generator

In object tracking applications, it is common for trackers to experience drift problems when the object of interest becomes deformed, which compromises the ability of the tracker to track the object. It is therefore desirable to develop a learning tracker classifier that is robust to deformations. The performance of existing trackers that employ deep classification networks degrades when the amount of training data is limited and does not cover all possible scenarios. While these limitations can be mitigated in part by using larger training datasets, these datasets may still not cover all situations and the positive samples are still monotonous. To overcome this problem, we propose a novel deformation samples generator that generates samples that would normally be difficult for the tracker to classify. In the proposed framework, both the classifier and deformation samples generator learn in a joint manner. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for the visual object tracking task.     

Robust online tracking via sparse gradient convolution networks

Convolution networks trained offline have recently exhibited promising performance in object tracking tasks. However, offline training is time-consuming and their performance heavily rely on the category of auxiliary training sets. In this paper, we propose a sparse gradient convolution network without pretraining for object tracking. This approach combines shallow convolutional networks and traditional methods (gradient features and sparse representations) to avoid the offline training. In the first frame, we utilize the sparse representation method to learn a series of gradient-based local patches served as fixed filters, and they are used to convolving the input image in the subsequent frames to encode local structural information. Then, we stack all the local structure features to construct global spatial structure features, and the inner geometric layout information is preserved. Moreover, sparse coding and online updating are used to overcome issues related to target appearance variations. Qualitative and quantitative evaluations based on a challenging benchmark dataset demonstrate the effectiveness of the proposed algorithm against several state-of-the-art tracking methods.     

Robust shape tracking with multiple models in ultrasound images

This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing their influence on the shape estimates. The problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem. The heart motion presents two phases (diastole and systole) with different dynamics, the multiple models used in this tracker try to solve this difficulty. In addition, ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models. Robust estimation techniques are used to address this difficulty. The multiple model data association (MMDA) tracker proposed in this paper is based on a bank of nonlinear filters, organized in a tree structure. The algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques.     

Robust multi-scale ship tracking via multiple compressed features fusion

In this paper, we address the problem of tracking a single ship in inland waterway closed circuit television (CCTV) video sequences given its location in the first frame and no other prior information. First, based on the compressive sensing theory, we employ two kinds of random measurement matrices to extract two complementary good features to track the target ship. Second, in order to track both location and scale, we construct our random measurement matrices according to spatial and temporal structure constraints in consecutive frames, which can be easily obtained and recorded in an offline manner. Having obtained the low-dimensional features in the compressed domain, we further take the different discriminability strengths of the extracted features into account and perform feature evaluations through their cumulative classification performances. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background and a coarse-to-fine strategy is adopted to speed up the time-consuming detection procedure. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm outperforms several state-of-the-art methods in terms of accuracy, precision and robustness     

Robust rate-control for wavelet-based image coding via conditional probability models.

Real-time rate-control for wavelet image coding requires characterization of the rate required to code quantized wavelet data. An ideal robust solution can be used with any wavelet coder and any quantization scheme. A large number of wavelet quantization schemes (perceptual and otherwise) are based on scalar dead-zone quantization of wavelet coefficients. A key to performing rate-control is, thus, fast, accurate characterization of the relationship between rate and quantization step size, the R-Q curve. A solution is presented using two invocations of the coder that estimates the slope of each R-Q curve via probability modeling. The method is robust to choices of probability models, quantization schemes and wavelet coders. Because of extreme robustness to probability modeling, a fast approximation to spatially adaptive probability modeling can be used in the solution, as well. With respect to achieving a target rate, the proposed approach and associated fast approximation yield average percentage errors around 0.5% and 1.0% on images in the test set. By comparison, 2-coding-pass rho-domain modeling yields errors around 2.0%, and post-compression rate-distortion optimization yields average errors of around 1.0% at rates below 0.5 bits-per-pixel (bpp) that decrease down to about 0.5% at 1.0 bpp; both methods exhibit more competitive performance on the larger images. The proposed method and fast approximation approach are also similar in speed to the other state-of-the-art methods. In addition to possessing speed and accuracy, the proposed method does not require any training and can maintain precise control over wavelet step sizes, which adds flexibility to a wavelet-based image-coding system.     

Robust visual tracking via CAMShift and structural local sparse appearance model

This paper addresses issues in visual tracking where videos contain object intersections, pose changes, occlusions, illumination changes, motion blur, and similar color distributed background. We apply the structural local sparse representation method to analyze the background region around the target. After that, we reduce the probability of prominent features in the background and add new information to the target model. In addition, a weighted search method is proposed to search the best candidate target region. To a certain extent, the weighted search method solves the local optimization problem. The proposed scheme, designed to track single human through complex scenarios from videos, has been tested on some video sequences. Several existing tracking methods are applied to the same videos and the corresponding results are compared. Experimental results show that the proposed tracking scheme demonstrates a very promising performance in terms of robustness to occlusions, appearance changes, and similar color distributed background.     

Robust face detection and analysis

This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations.     

Robust object tracking based on adaptive templates matching via the fusion of multiple features

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms.     

Robust tracking control of mechatronic arms

A robust tracking control scheme based on variable structure systems (VSS) theory is presented to cope with the uncertainties and parameter variations in mechatronic arm dynamics. A modification of VSS is used to remove its restrictions with regard to chattering and required control efforts. By blending VSS with a self-organizing controller (SOC), a sliding mode self-organizing controller (SLIMSOC)scheme has been developed. In this scheme, both control actions and performance evaluation are executed using the distance from the desired sliding surface and rate of approach to it. Comparisons are drawn and it is shown that the inherent robustness properties of variable structure systems are retained while the undesirable chatter motion of the sliding mode is eliminated. The results are illustrated by applications of SLIMSOC on a direct drive SCARA type of robot.     

A generic approach to simultaneous tracking and verification in video

A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H/sub 1/ and H0, we first estimate posterior probabilities P(H/sub 0/|Z) and P(H/sub 1/|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.     

A generic framework for tracking using particle filter with dynamic shape prior.

Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and background.     

Check! a generic and specific industrial inspection tool

The authors present an overview of an `inspection and control' package to perform automatic quality control of industrial components, specifically electronic circuit boards. This application has grown out of a set of tools designed initially to aid the development of point distribution models (PDMs) and grey-level models (GLMs) pioneered at the Wolfson Image Analysis Unit in Manchester. These models are both generic and specific. Generic, because they can be applied to most image classification problems; specific, because they aim at the full interpretation of the variability of the objects to be modelled. The underlying vision processing techniques are based on statistical pattern matching. Two synergetic approaches have been followed. The system is trained to recognise the components from either their shape or their grey level appearance or both     

Frequency tracking of atrial fibrillation using hidden Markov models.

A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.     

Joint face and head tracking inside multi-camera smart rooms

The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional (2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in the available frame views. This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the IBM T.J. Watson Research Center.     

Multiple target tracking and multiple frequency line tracking usinghidden Markov models

The authors consider the application of hidden Markov models (HMMs) to the problem of multitarget tracking-specifically, to the problem of tracking multiple frequency lines. The idea of a mixed track is introduced, a multitrack Viterbi algorithm is described and a detailed analysis of the underlying Markov model is presented. Simulations show that in some cases, it is possible to avoid data association and directly compute the maximum a posteriori mixed track. Some practical aspects of the algorithm are discussed and simulation results, presented     

Robust contour matching via the order-preserving assignment problem.

A common approach to determining corresponding points on two shapes is to compute the cost of each possible pairing of points and solve the assignment problem (weighted bipartite matching) for the resulting cost matrix. We consider the problem of solving for point correspondences when the shapes of interest are each defined by a single, closed contour. A modification of the standard assignment problem is proposed whereby the correspondences are required to preserve the ordering of the points induced from the shapes' contours. Enforcement of this constraint leads to significantly improved correspondences. Robustness with respect to outliers and shape irregularity is obtained by required only a fraction of feature points to be matched. Furthermore, the minimum matching size may be specified in advance. We present efficient dynamic programming algorithms to solve the proposed optimization problem. Experiments on the Brown and MPEG-7 shape databases demonstrate the effectiveness of the proposed method relative to the standard assignment problem.     

结构化道路车道线的鲁棒检测与跟踪

针对智能车在视觉导航过程中车道线检测的鲁棒性和实时性问题,提出一种适用于结构化道路的车道线鲁棒检测与跟踪方法。首先,简化的Sobel算子提取车道线边缘图像,将边缘图像与改进的Otsu方法得到的车道线分割图像进行融合,实现对车道线标记点的鲁棒检测;然后,采用迭代最小二乘方法拟合车道线标记点并去除干扰点,并根据拟合参数建立车道线模型;最后,引入尺度无迹卡尔曼滤波(SUKF)对车道线进行跟踪。通过对多段实地采集的视频进行了仿真实验,结果表明,该方法对于高速公路车道线的检测率可达到99%,并具有较好实时性能;对于受损和弄污的城市道路车道线也体现出较好的鲁棒性和时间性能。     

Saliency model-based face segmentation and tracking in head-and-shoulder video sequences

In this paper, a novel face segmentation algorithm is proposed based on facial saliency map (FSM) for head-and-shoulder type video application. This method consists of three stages. The first stage is to generate the saliency map of input video image by our proposed facial attention model. In the second stage, a geometric model and an eye-map built from chrominance components are employed to localize the face region according to the saliency map. The third stage involves the adaptive boundary correction and the final face contour extraction. Based on the segmented result, an effective boundary saliency map (BSM) is then constructed, and applied for the tracking based segmentation of the successive frames. Experimental evaluation on test sequences shows that the proposed method is capable of segmenting the face area quite effectively.     

Robust vision tracking by online random ferns and template library

In this paper, we proposed a robust tracking algorithm with an appearance model based on random ferns and template library. We adopt random Gaussian difference to generate binary features which depend on two randomly selected points and their corresponding Gaussian blur kernels. Semi-naive Bayes based random ferns are adopted as the discriminative model, and a template library including both positive templates and negative templates is used as generative model, the co-training of both discriminative and generative models gives our tracker the ability to separate foreground and background samples accurately. Besides, we also come up with a fragment based method which combines global ferns and local ferns to handle the occlusion problem. Experimental results demonstrated that the proposed algorithm performs well in terms of accuracy and robustness.