Effective Appearance Model Research Articles

Multiple Reliable Structured Patches for Object Tracking

It is essential to build the effective appearance model for object tracking in computer vision. Most object trackers can be roughly divided into two categories according to the appearance model: the bounding box model and the patch model. The bounding box model cannot handle shape deformation and occlusion of the non-rigid moving object effectively. The patch model is prone to be disturbed by complex backgrounds. In this paper, we propose a robust multi-structured-patch appearance model to represent the target for object tracking. The proposed appearance model is aimed to exploit and identify reliable patches that can be tracked effectively through the whole tracking process. According to attention mechanism in biological vision system, a coarse-to-fine strategy is usually used to search the target. Therefore, the proposed appearance model is represented by robust patches in different sizes, in which the bigger patches search the rough region of the target and the smaller patches estimate the accurate location. Experimental results on OTB100 dataset show that the proposed method outperforms state-of-the-art trackers.

Robust Visual Tracking Based on Relaxed Target Representation

Developing an effective target appearance model is a challenging task in visual tracking under the influences of complicated appearance variations. Many tracking algorithms use a linear combination of previous tracking results to represent a target candidate. In existing target representations, all of the feature elements of a target candidate have the same coding vector. With such type of target representations, robust tracking is not satisfactory when drastic appearance variations occur. In this work, we present a novel appearance model for visual tracking. The proposed appearance model considers the similarity and the distinctiveness of the feature elements of a target candidate. The feature elements should share some similarity to jointly represent a target pattern. We exploit the distinctiveness of feature elements to represent the different importance by introducing a weighted regularization term in the appearance model. A more stable and discriminative target representation is obtained. Superior performance on challenging sequences against state-of-the-art trackers show the robustness of the novel appearance model and the proposed tracker.

Effective appearance model update strategy in object tracking

Robust and accurate tracking for fast moving object in correlation filter framework is a challenging research problem. The appearance model update strategy impact on performance is usually very significant and hence is worth studying. Unfortunately, very few works focus on this component. This study proposes an adaptive appearance model update method, which utilises both average peak‐to‐correlation energy (APCE) threshold and gradient APCE threshold to measure tracking reliability. When tracking is unreliable, the update rate is modified and the initial template information is used to assist in correcting the model parameters. Different from conventional methods, both APCE threshold and gradient APCE threshold are enhanced by weighting the coefficients. More importantly, the gradient APCE threshold can capture the rapid decline of APCE which implies that the object is in fast motion. Sufficient evaluations on OTB datasets demonstrate that the proposed method can tackle challenging videos well, especially the authors obtain a gain of 4.6% in precision score on the selected 32 videos with fast motion attribute, and a mean gain of 3.0 and 2.2% in precision and success plots on OTB50, compared with the baseline tracker SAMF.

Mutation based self regulating and self perception particle swarm optimization for efficient object tracking in a video

Object tracking is a pre-eminent task in many computer vision application.This paper addresses a realistic object tracking problem, considering single as well as multiple objects with different challenging perspectives. In view of this, a Mutation-based Self Regulating and self-Perception Particle Swarm Optimization (MSRSP-PSO) algorithm is proposed in order to explore the effective sampling strategy as well as to estimate and localize the optimum position of the target object. In conjunction with the optimization technique, an effective appearance model is also used for the reinforcement of tracker to achieve better accuracy. The appearance model is constructed with the joint distribution of the Local Binary Pattern (LBP)/Local Contrast Measure (LCM) based texture description and the Ohta color features. In pursuance of validating the efficacy of the proposed tracking algorithm, performance of other state-of-the-art methods such as, Particle Swarm Optimization (PSO) based tracking, Sequential Particle Swarm Optimization (SPSO) based tracking, and Adaptive Gaussian Particle Swarm Optimization (AGPSO) based tracking algorithms are compared with the proposed tracking algorithm. Both the quantitative and qualitative evaluation of the experiments, validate the effectiveness of the proposed technique.The performance measures include the convergence rate, center location error and the coverage test (PASCAL SCORE). Furthermore, the non-parametric statistical analysis using the Sign test and Wilcoxon signed rank test, is also figured out to validate the statistical superiority of the proposed MSRSP-PSO algorithm over others. The experimental results reveal that the proposed algorithm robustly track single as well as multiple similar objects with complex interaction.

Dual‐scale weighted structural local sparse appearance model for object tracking

It is a great challenge to develop an effective appearance model for robust visual tracking due to various interfering factors, such as pose change, occlusion, background clutter etc. More and more visual tracking methods tend to exploit the local appearance model to deal with the above challenges. In this study, the authors present a simple yet effective weighted structural local sparse appearance model, which can better describe the target appearance information through patch‐based generative weight. To further improve the robustness of tracking, they implement this appearance model on two‐scale patches. The two derived appearance models are then combined to form a collaborative model to play their advantages. Extensive experiments on the tracking benchmark dataset show that the proposed method performs favourably against several state‐of‐the‐art methods.

Visual Tracking With Weighted Adaptive Local Sparse Appearance Model via Spatio-Temporal Context Learning.

Sparse representation has been widely exploited to develop an effective appearance model for object tracking due to its well discriminative capability in distinguishing the target from its surrounding background. However, most of these methods only consider either the holistic representation or the local one for each patch with equal importance, and hence may fail when the target suffers from severe occlusion or large-scale pose variation. In this paper, we propose a simple yet effective approach that exploits rich feature information from reliable patches based on weighted local sparse representation that takes into account the importance of each patch. Specifically, we design a reconstruction-error based weight function with the reconstruction error of each patch via sparse coding to measure the patch reliability. Moreover, we explore spatio-temporal context information to enhance the robustness of the appearance model, in which the global temporal context is learned via incremental subspace and sparse representation learning with a novel dynamic template update strategy to update the dictionary, while the local spatial context considers the correlation between the target and its surrounding background via measuring the similarity among their sparse coefficients. Extensive experimental evaluations on two large tracking benchmarks demonstrate favorable performance of the proposed method over some state-of-the-art trackers.

Visual tracking using locality‐constrained linear coding under a particle filtering framework

Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality-constrained linear coding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local-coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three-step matrix calculation, and the computational complexity of the proposed tracking algorithm is o ( η × m × n ) . Both quantitative and qualitative experimental results demonstrate that the authors’ proposed algorithm performs favourably against the 10 state-of-the-art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information.

Collaborative model with adaptive selection scheme for visual tracking

Visual tracking is a challenging task since it involves developing an effective appearance model to deal with numerous factors. In this paper, we propose a robust object tracking algorithm based on a collaborative model with adaptive selection scheme. Specifically, based on the discriminative features selected from the feature selection scheme, we develop a sparse discriminative model (SDM) by introducing a confidence measure strategy. In addition, we present a sparse generative model (SGM) by combining l1 regularization with PCA reconstruction. In contrast to existing hybrid generative discriminative tracking algorithms, we propose a novel adaptive selection scheme based on the Euclidean distance as the joint mechanism, which helps to construct a more reasonable likelihood function for our collaborative model. Experimental results on several challenging image sequences demonstrate that the proposed tracking algorithm leads to a more favorable performance compared with the state-of-the-art methods.

Robust visual tracking using information theoretical learning

This paper presents a novel online object tracking algorithm with sparse representation for learning effective appearance models under a particle filtering framework. Compared with the state-of-the-art l1 sparse tracker, which simply assumes that the image pixels are corrupted by independent Gaussian noise, our proposed method is based on information theoretical Learning and is much less sensitive to corruptions; it achieves this by assigning small weights to occluded pixels and outliers. The most appealing aspect of this approach is that it can yield robust estimations without using the trivial templates adopted by the previous sparse tracker. By using a weighted linear least squares with non-negativity constraints at each iteration, a sparse representation of the target candidate is learned; to further improve the tracking performance, target templates are dynamically updated to capture appearance changes. In our template update mechanism, the similarity between the templates and the target candidates is measured by the earth movers' distance(EMD). Using the largest open benchmark for visual tracking, we empirically compare two ensemble methods constructed from six state-of-the-art trackers, against the individual trackers. The proposed tracking algorithm runs in real-time, and using challenging sequences performs favorably in terms of efficiency, accuracy and robustness against state-of-the-art algorithms.

Adaptive Compressive Tracking via Online Vector Boosting Feature Selection.

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper, we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects. First, the most discriminative features are selected via an online vector boosting method. Second, the object representation is updated in an effective online manner, which preserves the stable features while filtering out the noisy ones. Furthermore, a simple and effective trajectory rectification approach is adopted that can make the estimated location more accurate. Finally, a multiple scale adaptation mechanism is explored to estimate object size, which helps to relieve interference from background information. Extensive experiments on the CVPR2013 tracking benchmark and the VOT2014 challenges demonstrate the superior performance of our method.

Robust visual tracking based on generative and discriminative model collaboration

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.

Scale-adaptive compressive tracking with feature integration

Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naive Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.

Fast deformable structure regression tracking

Visual object tracking is a challenging task because designing an effective and efficient appearance model is difficult. Current online tracking algorithms treat tracking as a classification task and use labelled samples to update appearance model. However, it is not clear to evaluate instance confidence belongs to the object. In this study, the authors propose a simple and efficient tracking algorithm with a deformable structure appearance. In their method, model updates with continuous labelled samples which are dense sampling. To improve the accuracy, they introduce a coupled-layer regression model which prevents negative background from impacting on the model learning rather than traditional classification. The proposed deformable structure regression tracker runs in real time and performs favourably against state-of-the-art trackers on various challenging sequences.

Robust object tracking by online Fisher discrimination boosting feature selection

Large appearance changes in visual tracking affect the tracking performance severely. To address this challenge, in this paper we develop an effective appearance model with the highly discriminative features. We propose an online Fisher discrimination boosting feature selection mechanism, which selects features that reduce the with-in scatter while enlarging the between-class scatter, thereby enhancing the discriminative capability between the target and background. Moreover, we utilize a particle filtering framework for visual tracking, in which the weights of candidate particles take into account the context information around the particles, thereby enhancing the robustness of tracking. In order to increase efficiency, a coarse-to-fine search strategy is exploited to efficiently and accurately locate the target. Extensive experiments on the CVPR2013 tracking benchmark demonstrate the competitive performance of our algorithm over other representative algorithms in terms of accuracy and robustness.

A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking

Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature saliency concept into the background modelling, and put forward a novel foreground saliency-based background-weighted histogram scheme (FSBWH) for target representation and tracking, which exploits salient features from both foreground and background. We think that background and foreground salient features are both crucial for target representation and tracking. Experimental results show that the proposed FSBWH scheme can improve the robustness and performance of tracker significantly especially in complex occlusions and similar background scenes.

Multi-scale ship tracking via random projections

Ship tracking plays a key role in inland waterway closed circuit television (CCTV) video surveillance. Although much success has been demonstrated in the construction of effective appearance model, numerous issues remain to be addressed due to factors such as pose and illumination change, partial or full occlusion, abrupt scale variation and motion blur. In this paper, we firstly inherit the intrinsical merits of subspace representation which demonstrates robustness to partial or full occlusion, pose and illumination variation. A very sparse measurement matrix is adopted to extract the features for the appearance model. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background. Secondly, in order to increase the randomness of the random projection matrix and further reduce memory load, we develop our ship appearance model based on fern features in the compressed domain. Thirdly, we track the scale by enhancing the tracker with a mechanism of feedback. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm achieves favorable performance in terms of efficiency and accuracy.

Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the PSO algorithm, which guides all particles towards the global optima state based on a fitness function. Then, the SIFT feature information is integrated into the iterative results of PSO to acquire a more accurate tracking state. Secondly, we present an effective appearance model updating criterion, which evaluates which fragments in appearance model need updating at each frame. However, the fragments with occluded parts or low quality measure values are not updated. The method for updating appearance model is introduced to improve the tracking performance. Compared with state-of-the-art algorithms, the proposed method can still stably track the target during the course of long-term partial occlusions using superior fragments of tracked target. The experiment results demonstrate the effectiveness of our algorithm in complex environments where the target object undergoes partial occlusions and large changes in pose and illumination.

Modeling 4D Changes in Pathological Anatomy using Domain Adaptation: Analysis of TBI Imaging using a Tumor Database.

Analysis of 4D medical images presenting pathology (i.e., lesions) is significantly challenging due to the presence of complex changes over time. Image analysis methods for 4D images with lesions need to account for changes in brain structures due to deformation, as well as the formation and deletion of new structures (e.g., edema, bleeding) due to the physiological processes associated with damage, intervention, and recovery. We propose a novel framework that models 4D changes in pathological anatomy across time, and provides explicit mapping from a healthy template to subjects with pathology. Moreover, our framework uses transfer learning to leverage rich information from a known source domain, where we have a collection of completely segmented images, to yield effective appearance models for the input target domain. The automatic 4D segmentation method uses a novel domain adaptation technique for generative kernel density models to transfer information between different domains, resulting in a fully automatic method that requires no user interaction. We demonstrate the effectiveness of our novel approach with the analysis of 4D images of traumatic brain injury (TBI), using a synthetic tumor database as the source domain.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

Online Object Tracking With Sparse Prototypes

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l(1) regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.