A novel foreground region analysis using NCP-DBP texture pattern for robust visual tracking

Robust visual tracking is the important stage in the computer vision applications such as robotics, man-free control systems, and the visual surveillance. Accurate motion states estimation and the target representation in visual tracking system are based on the appearances of the target. The factor affects the learning of target representation is the accumulated error due to the pose, illumination changes, and the uneven background. The presence of dynamic background and the shadowing effects causes the visual drift and destructive information. Besides, the misclassification of target region induces the false detection of moving objects. The K-means and Fuzzy-C-means clustering algorithms are available to segment the foreground/background and suppress the shadow region on the basis of the non-changing background of the surveillance area. This paper proposes the novel background normalization technique with textural pattern analysis to suppress the shadow region. The Neighborhood Chain Prediction (NCP) algorithm is used to cluster the uneven background and the Differential Boundary Pattern (DBP) extracts the texture of the video frame to suppress the shadow pixels present in the frame. The lower intensity estimation and the prediction of the area around the lower intensity in proposed work enhance the pixels for shadow removal. The shadow-free frame split up into several grids and the histograms of features are extracted from the grid formatted frame. Finally, the Machine Level Classification (MLC) finds the matching grid corresponds to the tracking region and provides the binary labeling to separate the background and foreground. The proposed DBP-based visual tracking system is high robustness over the sudden illumination changes and the dynamic background due to the texture pattern analysis. The comparison of proposed NCP-DBP combination with the existing segmentation techniques regarding the accuracy, precision, recall, F-measure, success and error rate assured the effectiveness in visual tracking applications.     

Multimodal MSEPF for visual tracking

Recently, particle filter has been applied to many visual tracking problems and it has been modified in order to reduce the computation time or memory usage. One of them is the Mean-Shift embedded particle filter (MSEPF, for short) and it is further modified as Randomized MSEPF. These methods can decrease the number of the particles without the loss of tracking accuracy. However, the accuracy may depend on the definition of the likelihood function (observation model) and of the prediction model. In this paper, the authors propose an extension of these models in order to increase the tracking accuracy. Furthermore, the expansion resetting method, which was proposed for mobile robot localization, and the changing the size of the window in Mean-Shift search are also selectively applied in order to treat the occlusion or rapid change of the movement.     

Active matching for visual tracking

In the feature matching tasks which form an integral part of visual tracking or SLAM (Simultaneous Localisation And Mapping), there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via ‘first get candidate matches, then resolve’ consensus algorithms such as RANSAC or JCBB. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with far fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm denoted Active Matching, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking with a range of settings, and give a detailed analysis of performance which leads to performance-enhancing approximations to the full algorithm.     

Dynamic active contours for visual tracking

Visual tracking using active contours is usually set in a static framework. The active contour tracks the object of interest in a given frame of an image sequence. A subsequent prediction step ensures good initial placement for the next frame. This approach is unnatural; the curve evolution gets decoupled from the actual dynamics of the objects to be tracked. True dynamical approaches exist, all being marker particle based and thus prone to the shortcomings of such particle-based implementations. In particular, topological changes are not handled naturally in this framework. The now classical level set approach is tailored for evolutions of manifolds of codimension one. However, dynamic curve evolution is at least a codimension two problem. We propose an efficient, level set based approach for dynamic curve evolution, which addresses the artificial separation of segmentation and prediction while retaining all the desirable properties of the level set formulation. It is based on a new energy minimization functional which, for the first time, puts dynamics into the geodesic active contour framework.     

Neural structures for visual motion tracking

This paper addresses visual motion tracking by a connectionist method, and aims at showing how the flexibility and the generalization power of neural networks can enhance a tracking system's adaptiveness and effectiveness. The simple principle of operation widens the range of applicability. A set of tracking structures that exhibit increasing levels of integration and efficiency are described. We also show how multinetwork architectures for estimate averaging may greatly increase tracking stability. The validity of the basic mechanism was assessed on a simple domain; however, a specific difficult testbed made it possible to verify the effectiveness of the method.     

Collaborative strategy for visual object tracking

Multimedia Tools and Applications - Adaptively learning the difference between object and background, discriminative trackers are able to overcome the complex background problem in visual object...     

Improved assignment with ant colony optimization for multi-target tracking

Detecting and tracking ground targets is crucial in military intelligence in battlefield surveillance. Once targets have been detected, the system used can proceed to track them where tracking can be done using Ground Moving Target Indicator (GMTI) type indicators that can observe objects moving in the area of interest. However, when targets move close to each other in formation as a convoy, then the problem of assigning measurements to targets has to be addressed first, as it is an important step in target tracking. With the increasing computational power, it became possible to use more complex association logic in tracking algorithms. Although its optimal solution can be proved to be an NP hard problem, the multidimensional assignment enjoyed a renewed interest mostly due to Lagrangian relaxation approaches to its solution. Recently, it has been reported that randomized heuristic approaches surpassed the performance of Lagrangian relaxation algorithm especially in dense problems. In this paper, impelled from the success of randomized heuristic methods, we investigate a different stochastic approach, namely, the biologically inspired ant colony optimization to solve the NP hard multidimensional assignment problem for tracking multiple ground targets.     

An improvement in MSEPF for visual tracking

Recently, many approaches to applying a particle filter to a visual tracking problem have been proposed. However, it is hard to implement such a filter in a real-time system because it requires a great deal of computation time and considerable resources to achieve a high accuracy. In order to overcome this difficulty, especially the computation time, Shan and other workers have proposed combining a particle filter and mean shift in order to maintain the accuracy with a small number of particles. In their approach, the state of each particle moves to the point in the window with the highest likelihood value. It is known that the accuracy of an estimation depends on the size of the window, but a larger window size makes the computation slower. In this article, we propose a method for exploring the highest likelihood more quickly by means of random sampling. Moreover, the likelihood is also modified in terms not only of color cues, but also of motion cues for a greater accuracy in object tracking. The effectiveness of the proposed method is evaluated by real image sequence experiments.     

A dual-kernel-based tracking approach for visual target

A dual-kernel-based tracking approach for visual target is proposed in this paper. The similarity between candidate and target model, and the contrast between candidate and its neighboring background are considered simultaneously when evaluating a target candidate. The similarity is measured by Bhattacharyya coefficient while the contrast is calculated with Jensen-Shannon divergence, and they are adaptively fused into a novel objective function. By maximizing the linear approximation of objective function, a dual-kernel target location-shift relation from current location to a new location is induced. According to the location-shift relation, the optimal target location can be recursively gained in the mean shift procedure. Experimental evaluations on several image sequences demonstrate that the proposed algorithm can gain more accurate target location and better identification power to false target, and it is also robust to deformation and partial occlusion.     

Sparse Bayesian learning for efficient visual tracking

This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic relevance vector machine (RVM) to generate observations with Gaussian distributions that can be fused over time. Rather than adapting a recognizer, we build a displacement expert which directly estimates displacement from the target region. An object detector is used in tandem, for object verification, providing the capability for automatic initialization and recovery. This approach is demonstrated in real-time tracking systems where the sparsity of the RVM means that only a fraction of CPU time is required to track at frame rate. An experimental evaluation compares this approach to the state of the art showing it to be a viable method for long-term region tracking.     

Graph-based transductive learning for robust visual tracking

In object tracking problem, most methods assume brightness constancy or subspace constancy, which are violated in practice. In this paper, the object tracking problem is considered as a transductive learning problem and a robust tracking method is proposed under intrinsic and extrinsic varieties. The object not only fits the object model, but also has the same cluster with the previous objects, which are the labeled data. By constraining the global and local information, the cost function is constructed firstly. The solution for minimizing the cost function can be solved by a simple linear algebra with graph Laplacian. Moreover, a novel graph is constructed over the positive samples and candidate patches, which can simultaneously learn the object's global appearance model and the local intrinsic geometric structure of all the patches. Furthermore, a heuristic positive samples selection scheme is adopted to make the method more effective. The proposed method is tested on different videos, which undergo large pose, expression, illumination and partial occlusion, and compared with state-of-the-art algorithms. Experimental results and comparative studies are provided to demonstrate the efficiency of the proposed method.     

A local-motion-based probabilistic model for visual tracking

Color-based tracking is prone to failure in situations where visually similar targets are moving in a close proximity or occlude each other. To deal with the ambiguities in the visual information, we propose an additional color-independent visual model based on the target's local motion. This model is calculated from the optical flow induced by the target in consecutive images. By modifying a color-based particle filter to account for the target's local motion, the combined color/local-motion-based tracker is constructed. We compare the combined tracker to a purely color-based tracker on a challenging dataset from hand tracking, surveillance and sports. The experiments show that the proposed local-motion model largely resolves situations when the target is occluded by, or moves in front of, a visually similar object.     

Robust online appearance models for visual tracking

We propose a framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects. The model adapts to slowly changing appearance, and it maintains a natural measure of the stability of the observed image structure during tracking. By identifying stable properties of appearance, we can weight them more heavily for motion estimation, while less stable properties can be proportionately downweighted. The appearance model involves a mixture of stable image structure, learned over long time courses, along with two-frame motion information and an outlier process. An online EM-algorithm is used to adapt the appearance model parameters over time. An implementation of this approach is developed for an appearance model based on the filter responses from a steerable pyramid. This model is used in a motion-based tracking algorithm to provide robustness in the face of image outliers, such as those caused by occlusions, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.     

Towards robust multi-cue integration for visual tracking

Abstract. Even though many of today's vision algorithms are very successful, they lack robustness, since they are typically tailored to a particular situation. In this paper, we argue that the principles of sensor and model integration can increase the robustness of today's computer-vision systems substantially. As an example, multi-cue tracking of faces is discussed. The approach is based on the principles of self-organization of the integration mechanism and self-adaptation of the cue models during tracking. Experiments show that the robustness of simple models is leveraged significantly by sensor and model integration.     

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.     

Correlation-based incremental visual tracking

Generative subspace models like probabilistic principal component analysis (PCA) have been shown to be quite effective for visual tracking problems due to their representational power that can capture the generation process for high-dimensional image data. The recent advance of incremental learning has further enabled them to be practical for real-time scenarios. Despite these benefits, the PCA-based approaches in visual tracking can be potentially susceptible to noise such as partial occlusion due to their compatibility judgement based on the goodness of fitting for the entire image patch. In this paper we introduce a novel appearance model that measures the goodness of target matching as the correlation score between partial sub-patches within a target. We incorporate the canonical correlation analysis (CCA) into the probabilistic filtering framework in a principled manner, and derive how the correlation score can be evaluated efficiently in the proposed model. We then provide an efficient incremental learning algorithm that updates the CCA subspaces to adapt to new data available from the previous tracking results. We demonstrate the significant improvement in tracking accuracy achieved by the proposed approach on extensive datasets including the large-scale real-world YouTube celebrity video database as well as the novel video lecture dataset acquired from British Machine Vision Conference held in 2009, where both datasets are challenging due to the abrupt changes in pose, size, and illumination conditions.     

Fuzzy chromatic co-occurrence matrices for tracking objects

Pattern Analysis and Applications - Tracking objects is an important field for many applications like driving assistance and video surveillance. Every tracking system should be able to track...