Set Of Target Templates Research Articles

Learning object-uncertainty policy for visual tracking

In research, we found that the purpose of most trackers is to obtain an accurate and robust score map, neglecting how to further examine the confidence of the results. Inspired by the Siamese trackers, which merely use the template from the first frame to locate the target, we propose a novel object-uncertainty policy. Firstly, we propose a dynamic design of the target template set for the tracked target, considering the initial target template and the reliable target template of the subsequent frames concurrently. Secondly, we adopt the multi-layer fusion to represent the target while analyzing the fusion of various feature layers. Moreover, we use a more effective cosine similarity function to calculate the similarity instead of the correlation operation. Finally, we propose a novel voting mechanism in accordance with the similarity between the target tracked in subsequent frames and the target template set. More importantly, this method can be embedded into DCF-like methods to improve tracking performance, which is embedded into the recent DiMP and PrDiMP trackers separately for comparison. Extensive experiments demonstrate that the discriminative ability of the model can be enhanced effectively by using our proposed method, capable of preventing the model from learning the background information. The code and raw tracking results are available at https://github.com/hexdjx/OUPT.

Learning spatio-temporal correlation filter for visual tracking

Correlation filter (CF) trackers have performed impressive performance with high frame rates. However, the limited information in both spatial and temporal domains is only used in the learning of correlation filters, which might limit the tracking performance. To handle this problem, we propose a novel spatio-temporal correlation filter approach, which employs both spatial and temporal cues in the learning, for visual tracking. In particular, we explore the spatial contexts from background whose contents are ambiguous to the target and integrate them into the correlation filter model for more discriminative learning. Moreover, to capture the appearance variations in temporal domain, we also compute a set of target templates and incorporate them into our model. At the same time, the solution of the proposed spatio-temporal correlation filter is closed-form and the tracking efficiency is thus guaranteed. Experimental experiments on benchmark datasets demonstrate the effectiveness of the proposed tracker against several CF ones.

Visual tracking based on robust appearance model

This paper aims at solving the problem of target drifting or losing due to the dramatic changes in the appearance of the target caused by the presence of severe target occlusion, background clutter, and illumination variations in video sequence. Firstly, we obtain a discriminative appearance description of the target by calculating the global and local Fisher vectors of the target area, and subsequently we utilize a semi-supervised linear kernel classifier to calculate the confidence of each candidate for well distinguish the foreground target from the background region. Meanwhile, as the change in the appearance of the object has caused serious pollution and interfered with the original information of the target area, we obtain the similarity between the sub-patches of the target template set and all the candidates, according to the pollution degree of patches of each candidate. Finally, we retrieve the best candidate according to the production of the weight, confidence and the similarity. The extensive experiment results show that our algorithm can effectively cope with the pollution of the target area and the drastic change of the target appearance caused by the severe occlusion and illumination changes in complex scenes. This also demonstrates that our algorithm has considerably accuracy and robustness in visual tracking.

Robust Structural Sparse Tracking.

Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

Visual Tracking via Sparse Representation with Reliable Structure Constraint

In this letter, we present a novel visual tracking algorithm based on sparse representation. In contrast to just use the target templates and the trivial templates to sparsely represent the target, we propose to further constrain the model with a set of discriminative weight maps. These weight maps contain the reliable structures of the target object. They help the model penalize the trivial template coefficients depending on the reliable structures of the target object. Then, the target object can be well represented by a sparse set of target templates together with a sparse set of target weight maps. We propose a unified objective function to integrate these two sparse representation problems together. This optimization problem can be well solved by the proposed iteration manner and a customized accelerated proximal gradient method. Furthermore, a novel weight map constructing method is proposed based on consistent motion property and forward–backward errors. Plenty of qualitative and quantitative evaluations demonstrate that our method performs favorably against the state-of-the-art methods in a wide range of tracking scenarios.

Robust mean shift tracking based on refined appearance model and online update

In this paper, a robust mean shift tracking algorithm based on refined appearance model (RAM) and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model to improve tracking precision and design an online update strategy to adjust to the appearance variation. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model construction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to distinguish the foreground from background. A weighted vector is then obtained based on the classified foreground from background, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on some challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance. A further comprehensive experiment on OTB2013 demonstrates that the proposed tracking algorithm outperforms the state-of-the-art works in most cases.

Adaptive Appearance Modeling with Point-to-Set Metric Learning for Visual Tracking

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.

Visual Tracking Based on Reversed Sparse Representation

In this paper, we propose a fast and robust tracking method based on reversed sparse representation. Be different from other sparse representation based visual tracking methods, the target template is sparsely represented by the candidate particles which are gotten by particle filter. In order to improve the robustness of the method, we use a target template set. Meanwhile, a two level competition mechanism is also introduced. In the first level, each target template is sparsely represented and all the candidate particles compete with each other by a similarity calculation, which is based on sparse coefficients. Then, the winners construct a target candidate set. In the second level, all the target candidates in the target candidate set compete with each other and the one which is the most similar to the template set is considered as the target. In addition, a template set update strategy is proposed to adapt the appearance variations of the target. Experimental results on challenging benchmark video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Robust visual tracking with discriminative sparse learning

Recently, sparse representation in the task of visual tracking has been obtained increasing attention and many algorithms are proposed based on it. In these algorithms for visual tracking, each candidate target is sparsely represented by a set of target templates. However, these algorithms fail to consider the structural information of the space of the target templates, i.e., target template set. In this paper, we propose an algorithm named non-local self-similarity (NLSS) based sparse coding algorithm (NLSSC) to learn the sparse representations, which considers the geometrical structure of the set of target candidates. By using non-local self-similarity (NLSS) as a smooth operator, the proposed method can turn the tracking into sparse representations problems, in which the information of the set of target candidates is exploited. Extensive experimental results on visual tracking have demonstrated the effectiveness of the proposed algorithm.