End-to-end DeepNCC framework for robust visual tracking

In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers.     

ACSiam: Asymmetric convolution structures for visual tracking with Siamese network

Object trackers based on Siamese network usually transform the tracking task into a matching problem between the candidate samples and the target template. However, with the increasing depth and width of backbone networks, researches on Siamese trackers using backbone networks are not very advanced. Therefore, it is necessary for us to further investigate the characteristics of backbone network. As a fact, the ability of backbone network to extract features can directly determine the performance of object tracker. Given this, in this paper, we first propose an asymmetric convolutional network to improve the representational capability of backbone network. And then, the strip convolution is employed to enhance the operational capability of square kernel convolution in the backbone network. Besides, we also construct a novel module named Feature Dropblock (i.e., FD) to simulate the occlusion of hidden space, which goal is to improve the performance of backbone network in the target tracking under occlusion. To demonstrate the effectiveness of the proposed tracker, extensive ablation studies are conducted. Better results are obtained on the tracking benchmarks OTB100 and VOT2018, compared to other state-of-the-art trackers.     

SiamMBFAN: Siamese tracker with multi-branch feature aggregation network

Siamese trackers have attracted considerable attention in the field of object tracking because of their high precision and speed. However, one of the main disadvantages of Siamese trackers is that their feature extraction network is relatively single. They often use AlexNet or ResNet50 as the backbone network. AlexNet is shallow and thus cannot easily extract abundant semantic information, whereas ResNet50 has many convolutional layers, reducing the real-time performance of Siamese trackers. We propose a multi-branch feature aggregation network with different designs in the shallow and deep convolutional layers. We use the residual module to build the shallow convolutional layers to extract textural and edge features. The deep convolution layers, designed with two independent branches, are built with residual and parallel modules to extract different semantic features. The proposed network has a depth of only nine modules, and thus it is a simple and effective network. We then apply the network to a Siamese tracker to form SiamMBFAN. We design multi-layer classification and regression subnetworks in the Siamese tracker by aggregating the last three modules of the two branches, improving the localization ability of the tracker. Our tracker achieves a better balance between performance and speed. Finally, SiamMBFAN is tested on four challenging benchmarks, including OTB100, VOT2016, VOT2018, and UAV123. Compared with other trackers, our tracker improves by 7% (OTB100).     

基于主分量寻踪的鲁棒视觉跟踪

传统子空间跟踪易受到模型漂移的影响而导致跟踪失败.针对此问题,本文提出一种基于主分量寻踪的鲁棒视觉跟踪方法.该方法以多个模板张成的子空间作为目标表观模型,利用主分量寻踪求解候选目标的误差分量,在粒子滤波框架下利用候选目标的误差分量估计最优状态参数.为了适应目标表观变化并克服模型漂移,本文提出一种模板更新方法.当跟踪结果与目标模板相似时,该方法利用跟踪结果更新目标模板,否则利用跟踪结果的低秩分量更新目标模板.在多个具有挑战性的图像序列上的实验结果表明:与现有跟踪方法相比,文中的跟踪方法具有较优的跟踪性能.     

结合全局光流的孪生区域提名网络目标跟踪算法

基于孪生网络的跟踪器受限于孪生网络跟踪框架固有的跟踪机制和搜索区域选择机制,当目标处在被遮挡、快速运动和出视野等困难场景下时,如何稳定、鲁棒地进行目标跟踪始终是孪生网络跟踪器亟需解决的问题。为此,文章提出一种结合光流的孪生区域提名网络目标跟踪算法(GOF-SiamRPN)。通过全局光流对目标的运动趋势信息进行补充,该方法可以有效地解决在这些困难场景下的跟踪问题。在VOT2019和UAV123上的实验结果表明,相比基准方法,该算法分别取得了2.0%和1.8%的性能提升。与其他先进的跟踪器相比,该算法也取得了有竞争力的跟踪效果。     

基于双模板Siamese网络的鲁棒视觉跟踪算法

近年来,Siamese网络由于其良好的跟踪精度和较快的跟踪速度,在视觉跟踪领域引起极大关注,但大多数Siamese网络并未考虑模型更新,从而引起跟踪错误。针对这一不足,该文提出一种基于双模板Siamese网络的视觉跟踪算法。首先,保留响应图中响应值稳定的初始帧作为基准模板R,同时使用改进的APCEs模型更新策略确定动态模板T。然后,通过对候选目标区域与2个模板匹配度结果的综合分析,对结果响应图进行融合,以得到更加准确的跟踪结果。在OTB2013和OTB2015数据集上的实验结果表明,与当前5种主流跟踪算法相比,该文算法的跟踪精度和成功率具有明显优势,不仅在尺度变化、平面内旋转、平面外旋转、遮挡、光照变化情况下具有较好的跟踪效果,而且达到了46 帧/s的跟踪速度。     

Learning complementary Siamese networks for real-time high-performance visual tracking

Recently, Siamese based methods have made a breakthrough in the visual tracking field. However, the existing trackers still cannot take full advantage of the deep features. In this work, we improve the performances of Siamese trackers by complementary learning with different types of matching features. Specifically, a Matching Activation Network (MAN) is firstly designed to highlight the matching regions of the search image given a template. Since only sparse parts of feature maps contribute to the matching result, an important design choice is to emphasize the weak-matching features by erasing the strong-matching ones and learn complementary classifiers from both types of features. Then we propose a novel complementary region proposal network (CoRPN) to take complementary features as inputs and their outputs complement to each other, which are fused to improve the performance. Experiments show that our proposed tracker achieves leading performances on five tracking datasets while retaining real-time speed.     

Siamese visual tracking with enriched semantics and dynamic template

Siamese tracking methods have recently drawn extensive attention due to their balanced accuracy and efficiency. However, most Siamese-based trackers use shallow backbone network, in which extracting high-level semantic features is difficult. When the appearance of distractors and targets is particularly similar, these methods may lead to tracking drift or even failure. Considering this deficiency, we propose a Siamese network with enriched semantics, named ESDT. First, a semantic enrichment module (SEM) comprising dilated convolution layers is designed to improve the classification capability of the siamese tracker. In addition, the target template is updated adaptively to cope with the target texture information changes caused by illumination and blur and further promote the tracking performance. Finally, exhaustive experimental analysis on the public datasets shows that the proposed algorithm outperforms several state-of-the-art algorithms and could track the target stably despite disturbances.     

基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪

L1跟踪对适度的遮挡具有鲁棒性,但是存在速度慢和易产生模型漂移的不足。为了解决上述两个问题,该文首先提出一种基于稀疏稠密结构的鲁棒表示模型。该模型对目标模板系数和小模板系数分别进行L2范数和L1范数正则化增强了对离群模板的鲁棒性。为了提高目标跟踪速度,基于块坐标优化原理,用岭回归和软阈值操作建立了该模型的快速算法。其次,为降低模型漂移的发生,该文提出一种在线鲁棒的字典学习算法用于模板更新。在粒子滤波框架下,用该表示模型和字典学习算法实现了鲁棒快速的跟踪方法。在多个具有挑战性的图像序列上的实验结果表明:与现有跟踪方法相比,所提跟踪方法具有较优的跟踪性能。     

Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.     

Edge-aware object pixel-level representation tracking

Recently, there has been a trend in tracking to use more refined segmentation mask instead of coarse bounding box to represent the target object. Some trackers proposed segmentation branches based on the tracking framework and maintain real-time speed. However, those trackers use a simple FCNs structure and lack of the edge information modeling. This makes performance quite unsatisfactory. In this paper, we propose an edge-aware segmentation network, which uses the complementarity between target information and edge information to provide a more refined representation of the target. Firstly, We use the high-level features of the tracking backbone network and the correlation features of the classification branch of the tracking framework to fuse, and use the target edge and target segmentation mask for simultaneous supervision to obtain an optimized high-level feature with rough edge information and target information. Secondly, we use the optimized high-level features to guide the low-level features of the tracking backbone network to generate more refined edge features. Finally, we use the refined edge features to fuse with the target features of each layer to generate the final mask. Our approach has achieved leading performance on recent pixel-wise object tracking benchmark VOT2020 and segmentation datasets DAVIS2016 and DAVIS2017 while running on 47 fps. Code is available at https://github.com/TJUMMG/EATtracker.     

自适应模板匹配在自主空中加油跟踪中的应用

自主空中加油技术越来越重要,在软管加油的对接阶段,运用模板匹配跟踪技术对目标进行跟踪。本文利用直方图均衡化、形态学算法和边缘轮廓提取得到初始模板;然后利用归一化相关匹配法对模板进行匹配,同时利用归一化相关值作为参考,判断是否对模板进行更新,从而得到自适应模板;为了缩小匹配时的搜索范围,加入了轨迹跟踪的算法,大大节省了运算时间。通过仿真实验显示,该算法切实可行。     

Affine hull based target representation for visual tracking

Handling appearance variations is a challenging issue in visual tracking. Existing appearance models are usually built upon a linear combination of templates. With such kind of representation, accurate visual tracking is not desirable when heavy appearance variations are in presence. Under the framework of particle filtering, we propose a novel target representation for tracking. Namely, the target candidates are represented by affine combinations of a template set, which leads to better capability in describing unseen target appearances. Additionally, in order to adapt this representation to dynamic contexts across a video sequence, a novel template update scheme is presented. Different from conventional approaches, the scheme considers both the importance of one template to a target candidate in the current frame and the recentness of the template that is kept in the template set. Comprehensive experiments show that the proposed algorithm achieves superior performances in comparison with state-of-the-art works.     

基于孪生神经网络的两阶段目标跟踪方法

深度学习技术使目标跟踪的精度和鲁棒性得到了很大提高,基于孪生网络的跟踪方法通过在大规模数据集上进行训练,使模型能应对目标的各种形变,缺点是无法排除相似目标的干扰。为此,提出了一种基于孪生网络的两阶段目标跟踪方法。首先,采用修改后的残差网络提取性能更优的深度特征。区域建议网络通过相关滤波调制自适应更新模板,结合时域信息过滤掉易区分的负样本;然后,通过感兴趣池化层提取候选区域固定尺度的特征,并馈送到验证网络进行更精细的分类与回归。为了提升网络对高难度样本的区分能力,采用正负样本对联合训练的方式提高特征匹配的性能。在OTB100、VOT标准测试集和UAV123无人机航拍数据集上进行了评测,实验结果表明:所提方法能明显改进基准算法的性能。     

Object tracking using discriminative sparse appearance model

Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms.     

Tracking with dynamic weighted compressive model

Fast compressive tracking utilizes a very sparse measurement matrix to capture the appearance model of targets. Such model performs well when the tracked targets are well defined. However, when the targets are low-grain, low-resolution, or small, a single fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the target. In this work, we propose a multi-sparse measurement matrices scheme along with a weight map to select the best measurement matrix that preserves the image structure of the targets during tracking. The weight map combines a contrast weight and a feature weight to efficiently characterize the target appearance and location. Moreover, a dispersion function is used for the online update of the target template, allowing tracking both the location and scale of the target. Extensive experimental results have demonstrated that the proposed DWCM tracking algorithm outperforms several state-of-the-art tracking algorithms as well as compressive tracker.     

基于视觉显著图在线更新的结构反稀疏目标跟踪算法

为提高稀疏跟踪器性能,提出一种在贝叶斯推论框架下的基于视觉显著图的结构反稀疏在线目标跟踪算法。首先将基于马尔可夫(Markov)模型的关联性视觉显著度检测算法用于当前帧并计算目标模板的显著图,其次提出全局与局部分块的结构外观模型表示候选目标,将显著图映射回每一个局部块并计算出对应的自适应权重,最后提出联合全局与局部稀疏解的度量准则度量候选目标与目标模板的相似度,从而确立在贝叶斯框架下对目标状态最佳估计。在跟踪过程中,采用反稀疏表达方式一次求解优化问题计算出所有粒子权重来提高算法效率。实验结果表明,本文算法具有良好的鲁棒性和实时性。      

基于kalman预测和自适应模板的目标相关跟踪研究

文中提出了一种基于kalman预测和自适应模板的目标相关跟踪算法。通过kalman预测下一帧图像中目标的状态,缩小整个图像上目标检测的搜索范围,满足目标跟踪的实时性。采取自适应模板更新策略．根据目标的变化情况自动调节参考模板,提高目标跟踪的稳定性。仿真实验结果表明,算法能够随着目标的形状、大小、位置的变化快速调整参考模板,进行稳定和实时的跟踪,当目标被物体遮挡时仍能有效地跟踪目标。     

Robust model adaption for colour-based particle filter tracking with contextual information

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.     

基于相关滤波的目标重检测跟踪

近年来，相关滤波方法由于具备运算速度快，鲁棒性强的优势，在目标跟踪领域发展迅速。然而，面对复杂场景时，现有模型难以满足实际需求。针对背景感知相关滤波方法(BACF)在目标发生自身旋转、尺度变换、运动出视野等挑战下，相关滤波器最大响应值减弱，造成跟踪精度下降的问题，提出了一种基于相关滤波的目标重检测跟踪方法。在原有背景感知相关滤波方法的基础上，引入滤波器响应检测机制，当判定到相关滤波跟踪结果不可信时，利用粒子滤波采样策略生成大量粒子，感知目标状态，重新确定目标中心位置。在此基础上，利用自适应尺度估计机制重新计算目标尺度信息，从而实现对目标的重新跟踪。为了验证改进算法的有效性，实验选取了OTB2013、OTB2015、VOT2016共3个公开数据集进行测试，同时与相关滤波及深度学习方法进行对比，从视频属性、跟踪精确度、算法鲁棒性等角度展示所有算法的性能。实验结果表明:基于相关滤波的目标重检测跟踪方法在3个公开数据集中取得较好的实验结果，并在目标发生旋转，尺度变换及运动超出视野的情况下，有效提高了BACF的准确率和成功率。