Visual tracking using IPCA and sparse representation

Building a robust and fully automatic framework for human motion tracking in 2D images and videos remains a challenging task in computer vision due to cluttered backgrounds, self-occlusions, variations of body shape and complexities of human postures. In this paper we propose a robust framework for human motion tracking without motion priors. The proposed framework builds an accurate/uncontaminated specific appearance model and then tracks the target’s postures with this specific appearance model. The main contribution of this work is a novel process to build an accurate appearance model by identifying non-target pixels and removing them. In addition, for the goal of tracking in multiple scales, a novel strategy for scale evaluation and adjustment is proposed to adaptively change the scale values during the tracking process. Experiments show that the accurate specific appearance model outperforms existing work, and the proposed tracking system is able to successfully track challenging sequences with different appearances, motions, scales and angles of view.

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).

Various sparse-representation-based tracking meth-ods have been proposed to tackle visual tracking problems, and most of them use simple intensity feature as the observation of target object. Moreover, most of them only take into account either global or local image representation and only exploit the underlying relationship among target candidates in a single frame. All of these may make their appearance models less robust to deal with complex scenes. To overcome these problems, we propose a smooth manifold kernel sparse tracker under the framework of particle filter. The proposed method characterizes targets and candidates with region covariance matrix descriptors, and constructs object tracking as a kernel sparse learning model based on symmetric positive-definite (SPD) manifolds. The spatial-temporal interdependencies among candidates and global-local representations of candidates are jointly considered and unified via the kernel sparse learning model. Moreover, in order to make the model more robust, the detection of outlier tasks is also taken into account. To handle the variation of object appearance, we develop a robust and efficient online dictionary learning algorithm on SPD manifolds. Extensive experiments on multiple benchmark datasets demonstrate that our tracker performs favorably against state-of-the-art trackers.

A new approach to align an image of a textured object with a given prototype (learned reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype (learned reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.

Effective object appearance model is one of the key issues for the success of visual tracking. Since the appearance of a target and the environment changes dynamically, the majority of existed visual tracking algorithms tend to drift away from targets. To address this issue, we propose a robust tracking algorithm by integrating the generative and discriminative model. The object appearance model is made up of generative target model and a discriminative classifier. For the generative target model, we adopt the weighted structural local sparse appearance model combining patch based gray value and Histogram of Oriented Gradients feature as the patch dictionary. By sampling positives and negatives, alignment-pooling features are obtained based on the patch dictionary through local sparse coding, then we use support vector machine to train the discriminative classifier. The proposed method is embedded into a Bayesian inference framework for visual tracking. A combined matching method is adopted to improve the proposal distribution of the particle filter. Moreover, in order to adapt the situation change, the patch dictionary and discriminative classifier are updated by incremental learning every five frames. Experimental results on some publicly available benchmarks of video sequences demonstrate the accuracy and effectiveness of our tracker.

Model-free tracking is a widely-accepted approach to track an arbitrary object in a video using a single frame annotation with no further prior knowledge about the object of interest. Extending this problem to track multiple objects is really challenging because: a) the tracker is not aware of the objects' type while trying to distinguish them from background (detection task), and b) The tracker needs to distinguish one object from other potentially similar objects (data association task) to generate stable trajectories. In order to track multiple arbitrary objects, most existing model-free tracking approaches rely on tracking each target individually by updating their appearance model independently. Therefore, in this scenario they often fail to perform well due to confusion between the appearance of similar objects, their sudden appearance changes and occlusion. To tackle this problem, we propose to use both appearance and motion models, and to learn them jointly using graphical models and deep neural networks features. We introduce an indicator variable to predict sudden appearance change and/or occlusion. When these happen, our model does not update the appearance model thus avoiding using the background and/or incorrect object to update the appearance of the object of interest mistakenly, and relies on our motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all targets simultaneously as a graphical model inference problem. We learn the joint parameters for both appearance model and motion model in an online fashion under the framework of LaRank. Experiment results show that our method achieved superior performance compared to the competitive methods.

A novel compressive local appearance model‐based object tracking algorithm is presented to address challenging issues in object tracking. To efficiently preserve image patches of an object and reduce the dimensionality, a random projection‐based feature selection method is introduced. Modelling the object's appearance using a sparse representation over a set of templates leads to an ℓ 1 ‐regularisation problem. To solve this problem, both the reconstruction error and the residual matrix are considered which play a key role in tracking an object with severe appearance variations using the modified likelihood function. Experimental results demonstrate that the proposed method outperforms existing state‐of‐the‐art tracking methods in terms of dealing with long‐term partial occlusion, deformation and rotation.

While many visual tracking methods have been proposed, the challenges such as severe deformation, cluttered background and heavy occlusion are still under well solved. This paper proposed a new visual tracking method by combing the sparse representation and global measure. We flrst construct an appearance model by sparse representation with the patches from target rectangles and their surrounding backgrounds. With this appearance model, we successfully identify the patches of target undergoing heavy occlusion or severe deformation. Then, we deflne a global measure based on the cues of saliency and color contrast to detect the tracking object. This measure performs well in distinguishing object from background especially in cluttered background. Many experiments on difierent challenge image sequences demonstrate that our method is much more robust and stable in tracking.

In discriminative tracking, lots of tracking methods easily suffer from changes of pose, illumination and occlusion. To deal with this problem, we propose a novel object tracking method using structural sparse representation-based semi-supervised learning and edge detection. First, the object appearance model is constructed by extracting sparse code features on different layers to exploit local information and holistic information. To utilize unlabelled samples information, the semi-supervised learning is introduced and a classifier is trained which is used to measure candidates. In addition, an auxiliary positive sample set is maintained to improve the performance of the classifier. We subsequently adopt an edge detection to alleviate the error accumulation based on the ranking results from the learned classifier. Finally, the proposed method is implemented under the Bayesian inference framework. Both the proposed tracker and several current trackers are tested on some challenging videos, where the target objects undergo pose change, illumination and occlusion. The experimental results demonstrate that the proposed tracker outperforms the other state-of-the-art methods in terms of effectiveness and robustness.

A consensus-based method for tracking: Modelling background scenario and foreground appearance

Sparse representation has been widely applied for modeling object appearance in the tracking process. Various sparsity models are coded by single feature with fixed local patches, which neglects the potential spatial information inside the appearance model. This way also gives a low robust effect on

Sparse coding based visual tracking: Review and experimental comparison

Our long term research goal is to develop an image-based approach for early diagnosis of lung nodules that may lead to lung cancer. This paper focuses on monitoring the progress of detected lung nodules in successive chest low dose CT (LDCT) scans of a patient using non-rigid registration. In this paper, we propose a new methodology for 3D LDCT data registration. The registration methodology is non-rigid and involves two steps: global alignment of one scan (target data) to another scan (reference data) using the learned prior appearance model followed by local alignments in order to correct for intricate deformations. From two subsequent chest scans, visual appearance of the chest images, after equalizing their signals, are modeled with a Markov-Gibbs random field with pairwise interaction. Our approach is based on finding the affine transformation to register one data set (target data) to another data set (reference data) by maximizing a special Gibbs energy function using a gradient descent algorithm. To get accurate appearance model, we developed a new approach to an automatically select the most important cliques that describe the visual appearance of LDCT data. To handle local deformations, we propose a new approach based on deforming each voxel over evolving closed and equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions minimizing distances between corresponding pixel pairs on the iso-surfaces on both data sets. Our preliminary results on 10 patients show that the proper registration could lead to precise identification of the progress of the detected lung nodules.

Real-time object tracking via self-adaptive appearance modeling

In order to realize robust visual tracking in natural environments, a novel algorithm based on adaptive appearance model is proposed. The model can adapt to changes in object appearance over time. A mixture of three Gaussian distributions models the value of each pixel. An online Expectation Maximization (EM) algorithm is developed to update the parameters of the Gaussians. The observation model in the particle filter is designed based on the adaptive appearance model. Numerous experimental results demonstrate that our proposed algorithm can track objects well under illumination change, large pose variation, and partial or full occlusion.Keywordsvisual trackingadaptive appearance modelExpectation Maximization algorithmparticle filter