Visual Tracking via Local Sparse Correlation Filters

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter-based models are susceptible to wrong updates stemming from inaccurate tracking results. To date, very little effort has been devoted towards handling the correlation filter update problem. In this paper, we propose a novel approach to address the correlation filter update problem. In our approach, we update and maintain multiple correlation filter models in parallel, and we use deep reinforcement learning for the selection of an optimal correlation filter model among them. To facilitate the decision process in an efficient manner, we propose a decision-net to deal with target appearance modeling, which is trained through hundreds of challenging videos using proximal policy optimization and a lightweight learning network. An exhaustive evaluation of the proposed approach on the OTB100 and OTB2013 benchmarks shows that the approach is effective enough to achieve the average success rate of 62.3% and the average precision score of 81.2%, both exceeding the performance of traditional correlation filter-based trackers.

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APCE-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

vertebral fracture is a very common outcome of osteoporosis, which is one of the major public health concerns in the world. Early detection of vertebral fractures is important because timely pharmacologic intervention can reduce the risk of subsequent additional fractures. Our goal seeks to develop a computerized method for detection of vertebral fractures by measuring the shape and appearance of vertebrae on cervical xray radiographs in order to assist radiologist’s image interpretation and thus allow the early diagnosis of osteoporosis. The statistical models of shape and appearance are powerful tools for interpreting medical images. This work introduces the application of correlation filter classifiers for identification and verification of the osteoporosis presence in cervical vertebrae training/ testing set. Correlation filter classifiers have been previously applied to other biometric classification tasks, but not to classification of cervical vertebrae images. We describe how the extraction of an appropriate region of interest in the cervical vertebrae surface can be used to design correlation filters that accomplish 90 % recognition on a database of 50 cervical bone shapes.Keywordsx-ray radiographsSegmentationASM modelingCorrelation filtervertebral deformityOsteoporosis

Accurate location estimation of a target is a classical and very popular problem in visual object tracking, for which correlation filters have been proven highly effective in real-time scenarios. However, the great variation of the target's appearance and the surrounding background throughout a video sequence would lead to failure tracking for the sake of the model drift, using trackers based on correlation filters. In our approach, we present a simple and fast method to improve the robustness of the model based on sum of template and pixel-wise learners (Staple). On the one hand, a confidence regression model is established to adjust adaptively the model online learning rate to alleviate the model drift. On the other hand, instead of likelihood, the scale with maximal posterior probability is selected as the target scale to obtain the more accurate estimation. Extensive experimental results demonstrate that the proposed approach performs favorably against several state-of-the-art algorithms on large-scale challenging benchmark data sets at speed in excess of 42 frames/s.

In visual tracking, developing an efficient appearance model is a challenging task due to the influence of various factors, such as illumination variation, occlusion, background clutter, and so on. Existing tracking algorithms use appearance samples from previous frames to form a template set upon which target appearance models are built. However, these appearance models are data-dependent, so they may be corrupted by significant appearance variation. It is difficult to update the templates in challenging environments. In this paper, we propose a robust visual tracking algorithm with an adaptive appearance model using a point-to-set metric learning technique. To do this, we first model a target representation using a set of target templates and a regularized affine hull (RAH) spanned by the target templates. Then, we learn a point-to-set distance metric, which is incorporated into the optimization process to obtain an adaptive target representation. The RAH model covers unseen target appearances by affine combinations of the target templates. Based on the proposed target appearance model, we design an effective template update scheme by adjusting the weights of the target templates. Experimental results on challenging video sequences with comparisons to several state-of-the-art tracking algorithms demonstrate the effectiveness and robustness of the proposed tracking algorithm.

In this paper, we propose a novel tracking method by formulating tracking as a correlation filtering as well as a ridge regression problem. First, we develop a tight correlation filter-based tracking framework from the signal detection perspective. In this formulation, the correlation filter is set as the same size as the target, which can make full use of the relations of the adjacent image patches and effectively exclude the influence of the background. Specifically, we point out that the novel correlation filter model can be regarded as the ridge regression model which takes into account the different importance of the samples and has the consistent objective with tracking. Second, we focus on the scale variation problem in tracking. By making use of the spatial structure of the correlation filter, the multiscale filter banks can be generated via interpolation to handle the scale estimation problem easily. Third, we present a novel distance importance-based confidence calculation model to determine the final tracking result, which not only makes use of the fine discriminability of the correlation filter but also takes the distance importance of the candidate samples into account to alleviate the impact of similar distractors. Experimental results demonstrate that our method is superior to several state-of-the-art trackers and many other correlation filter-based methods in the benchmark datasets.

The paper demonstrates a technique for analysing the following three problems. They are mainly associated with the automatic extraction of moving objects, suppression of the remaining errors, and tracking analysis under disturbed image situations from static camera. For this technique, a modified difference image-based approach for the segmentation of moving objects in video sequences is applied. The second part of the paper examines the problem of suppression of the remaining errors (holes, outliers or fusion of regions) by means of morphological and separation operators. The extracted image regions represent the object candidates for the following tracking. The efficiency of this suggested technology for moving objects segmentation will be demonstrated in this paper on the basis of the analysis of strongly disturbed image sequences.

Context-aware long-term correlation tracking with hierarchical convolutional features

Significant progress is made in the field of object tracking recently. Especially, trackers based on deep learning and correlation filters both have achieved excellent performance. However, object tracking still faces some challenging problems such as deformation and illumination. In such kinds of situations, the accuracy and precision of tracking algorithms plunge as a result. It is imminent to find a solution to this situation. In this paper, we propose a tracking algorithm based on features extracted by residual network called Resnet features and cascaded correlation filters to improve precision and accuracy. Firstly, features extracted by a deep residual network trained on other image processing datasets, are robust enough and retain higher resolution, therefore, we exploit Resnet-101 pretrained offline to obtain features extracted by middle and high layers for target appearance model representation. Resnet-101 is deeper compared with other deep neural networks which means it contains more semantic information. Then, the method we propose to combine our correlation filters is superior. We propose cascaded correlation filters generated by handcraft, middle-level and high-level features from residual network to gain better competence. Handcraft features localize target precisely because they contain more spatial details while Resnet features are robust to the target appearance change because they retain more semantic information. Finally, we conduct extensive experiments on OTB2013 and OTB2015 benchmark. The experimental results show that our tracker achieves high performance under all kinds of challenges and performs favorably against other state-of-the-art trackers.

Recently, the correlation filter (CF)-based methods have achieved great success in the field of object tracking. In most of these methods, the CF utilizes L 2 norm as the regularization, which does not pay attention to the stability and robustness of the feature. However, there may exist some unstable points in the image because the object in the video may have different appearance changes. We propose a tracking method based on a structured robust correlation filter (SRCF), which employs the L 2,1 norm as the regularization. The robust CF can not only retain the accuracy from the regression formulation but also take into account the stability of the image region to improve the robustness of the appearance model. The alternating direction method of multipliers algorithm is used to solve the L 2,1 optimization problem in SRCF. Moreover, the multilayer convolutional features are adopted to further improve the representation accuracy. The proposed method is evaluated in several benchmark datasets, and the results demonstrate that it can achieve comparable performance with respect to the state-of-the-art tracking methods.

In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Track lets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent track lets that contain the same target so that robust long-term tracking results can be achieved. As the appearance of the same target may change greatly due to heavy occlusion, pose variations and changing lighting conditions, a discriminative appearance model is crucial for association-based tracking. Unlike most previous methods which simply use the similarity of color histograms or other low level features to construct the appearance model, we propose to use the fusion of soft biometrics generated from sub-track lets to learn a discriminative appearance model in an online manner. Compared to low level features, soft biometrics are robust against appearance variation. The experimental results demonstrate that our method is robust and greatly improves the tracking performance over the state-of-the-art method.

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.

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.