In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers.
Dalian University of Technology（大连理工大学）；National University of Defense Technology（国防科技大学）；Peng Cheng Laboratory（鹏城实验室）
In recent years, target tracking has made great progress in accuracy. This development is mainly attributed to powerful networks (such as transformers) and additional modules (such as online update and refinement modules). However, less attention has been paid to tracking speed. Most state-of-the-art trackers are satisfied with the real-time speed on powerful GPUs. However, practical applications necessitate higher requirements for tracking speed, especially when edge platforms with limited resources are used. In this work, we present an efficient tracking method via a hierarchical cross-atten…
Nanyang Technological University（南洋理工大学）；Advanced Digital Science Center（先进数字科学中心）；City University of Hong Kong（香港城市大学）
In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to …
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing FaceAll Co.（北京飞搜科技有限公司）
For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information. In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT). The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion. Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal …
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution. Typically, these methods use two separated branches within a single network to accomplish detection and Re-ID respectively without studying the inter-relationship between them, which inevitably impedes the tracking performance. In this paper, we propose an online multi-object tracking framework based on a hierarchical single-branch network to solve this problem. Specifically, the proposed single-branch ne…
Harbin Institute of Technology Shenzhen Graduate School（哈尔滨工业大学深圳研究生院）；Harbin Institute of Technology（哈尔滨工业大学）
Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the between-class difference of the arbitrary objects, while the tracking task mainly deals with the within-class difference of the same objects. In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task. We propose a TIR tracker…
Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features partic…
Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Alibaba AI Labs（阿里巴巴人工智能实验室）
Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural…
National Institute of Informatics（国立信息学研究所）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
Current approaches in Multiple Object Tracking (MOT) rely on the spatio-temporal coherence between detections combined with object appearance to match objects from consecutive frames. In this work, we explore MOT using object appearances as the main source of association between objects in a video, using spatial and temporal priors as weighting factors. We form initial tracklets by leveraging on the idea that instances of an object that are close in time should be similar in appearance, and build the final object tracks by fusing the tracklets in a hierarchical fashion. We conduct extensive ex…
Peng Cheng Laboratory（鹏城实验室）；Anhui University（安徽大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）
Tracking-by-detection is a very popular framework for single object tracking which attempts to search the target object within a local search window for each frame. Although such local search mechanism works well on simple videos, however, it makes the trackers sensitive to extremely challenging scenarios, such as heavy occlusion and fast motion. In this paper, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with tracking-by-detection framework to conduct joint local and global search for robust tracking. Specifically, we extract the features of …
Yazd University（亚兹德大学）；Sharif University of Technology（谢里夫理工大学）；University of Alberta（阿尔伯塔大学）
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking. However, these methods cannot suitably model the target appearance due to the exploitation of hand-crafted features. On the other hand, the recent deep learning-based visual tracking methods have provided a competitive performance along with extensive computations. In this paper, an adaptive background-aware correlation filter-based tracker is proposed that effectively models the target appearance by using either the histogram of oriented gradients (HOG) or convolu…
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Network System and Network Culture（网络系统与网络文化北京市重点实验室）；Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism（交互技术与体验系统文化和旅游部重点实验室）
HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
KTH Royal Institute of Technology, Stockholm, Sweden（皇家理工学院）；College of Computer Science, Nankai University, Tianjin, China（南开大学计算机学院）；IIS, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院信息与系统研究所）；Nanjing University of Aeronautics and Astronautics, Nanjing, China（南京航空航天大学）
More powerful feature representations derived from deep neural networks benefit visual tracking algorithms widely. However, the lack of exploitation on temporal information prevents tracking algorithms from adapting to appearances changing or resisting to drift. This paper proposes a correlation filter based tracking method which aggregates historical features in a spatial-aligned and scale-aware paradigm. The features of historical frames are sampled and aggregated to search frame according to a pixel-level alignment module based on deformable convolutions. In addition, we also use a feature …
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学网络系统与控制研究所）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）
Autonomous robots enjoy a wide popularity nowadays and have been applied in many applications, such as home security, entertainment, delivery, navigation and guidance. It is vital to robots to track objects accurately in these applications, so it is necessary to focus on tracking algorithms to improve the robustness and accuracy. In this paper, we propose a robust object tracking algorithm based on a hierarchical ensemble framework which can incorporate information including individual pixel features, local patches and holistic target models. The framework combines multiple ensemble models sim…
A saliency guided hierarchical visual tracking (SHT) algorithm containing global and local search phases is proposed in this paper. In global search, a top-down saliency model is novelly developed to handle abrupt motion and appearance variation problems. Nineteen feature maps are extracted first and combined with online learnt weights to produce the final saliency map and estimated target locations. After the evaluation of integration mechanism, the optimum candidate patch is passed to the local search. In local search, a superpixel based HSV histogram matching is performed jointly with an L2…
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
University of Surrey（萨里大学）；Jiangnan University（江南大学）
Siamese approaches have achieved promising performance in visual object tracking recently. The key to the success of Siamese trackers is to learn appearance-invariant feature embedding functions via pair-wise offline training on large-scale video datasets. However, the Siamese paradigm uses one-shot learning to model the online tracking task, which impedes online adaptation in the tracking process. Additionally, the uncertainty of an online tracking response is not measured, leading to the problem of ignoring potential failures. In this paper, we advocate online adaptation in the tracking stag…
University of Alberta（阿尔伯塔大学）；Yazd University（亚兹德大学）；Sharif University of Technology（谢里夫理工大学）
We consider the problem of tracking an unknown small target from aerial videos of medium to high altitudes. This is a challenging problem, which is even more pronounced in unavoidable scenarios of drastic camera motion and high density. To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy. The proposed network fully exploits target-related information by multi-scale feature learning and attention modules. The proposed strategy introduces an efficient sampling strategy …
Hangzhou Dianzi University（杭州电子科技大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）
Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA. Within this paradigm, a series of simple, yet effective localization-aware components are introduced, to generate localization-aware target confidence scor…
HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科学教育创新园）；Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology（湖北省交通运输物联网重点实验室，计算机科学与人工智能学院，武汉理工大学）；State Key Laboratory for Multimedia Information Processing, Peking University（多媒体信息处理国家重点实验室，北京大学）
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation, low illumination, background clutter and occlusion. In particular, we employ the operations of max pooling to transform th…
Seoul National University（首尔国立大学）；Chung-Ang Univeristy（中央大学）
In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters, which involve solving complex optimization tasks to adapt to the new appearance of a target object. To alleviate this complex process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the t…
Carnegie Mellon University（卡内基梅隆大学）；Queensland University of Technology（昆士兰科技大学）
Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conven…
Shanghai Jiao Tong University（上海交通大学）；Australian Centre for Robotic Vision, The University of Adelaide（阿德莱德大学澳大利亚机器人视觉中心）；Virginia Tech（弗吉尼亚理工学院暨州立大学）；University of California, Merced（加州大学默塞德分校）
In this paper, we propose to exploit the rich hierarchical features of deep convolutional neural networks to improve the accuracy and robustness of visual tracking. Deep neural networks trained on object recognition datasets consist of multiple convolutional layers. These layers encode target appearance with different levels of abstraction. For example, the outputs of the last convolutional layers encode the semantic information of targets and such representations are invariant to significant appearance variations. However, their spatial resolutions are too coarse to precisely localize the tar…
Sensetime Group Limited（商汤集团有限公司）；Beihang University（北京航空航天大学）；The University of Sydney（悉尼大学）
In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besides, for better association, we propose switcher-aware classification (SAC), which takes the potential identity-switch causer (switcher) into consideration. Specifically, the proposed framework includes a Single Object Tracking (SOT) sub-net to capture short term cues, a re-identification (ReID) sub-net to extract long term cues and a switcher-aware classifier to make matching decisions using extracted features from t…
Seoul National University（首尔国立大学）；Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；NAVER Corp.（NAVER公司）
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encode…
Southern University of Science and Technology（南方科技大学）；University of Birmingham（伯明翰大学）；University of Electronic Science and Technology of China（电子科技大学）
With the development of depth sensors in recent years, RGBD object tracking has received significant attention. Compared with the traditional RGB object tracking, the addition of the depth modality can effectively solve the target and background interference. However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored. On the other hand, some methods attempt to fuse the two modalities by treating them equally, resulting in the missing of modality-specific features. To tackle these limitations, we propose a …
RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day and all-weather work. However, how to effectively represent RGBT data for visual tracking remains unstudied well. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored and exploited in RGBT tracking. In this paper, we propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target represent…
Recent progresses in model-free single object tracking (SOT) algorithms have largely inspired applying SOT to \emph{multi-object tracking} (MOT) to improve the robustness as well as relieving dependency on external detector. However, SOT algorithms are generally designed for distinguishing a target from its environment, and hence meet problems when a target is spatially mixed with similar objects as observed frequently in MOT. To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models…
Gottfried Wilhelm Leibniz Universität Hannover（汉诺威莱布尼茨大学）
Multiple people tracking is a key problem for many applications such as surveillance, animation or car navigation, and a key input for tasks such as activity recognition. In crowded environments occlusions and false detections are common, and although there have been substantial advances in recent years, tracking is still a challenging task. Tracking is typically divided into two steps: detection, i.e., locating the pedestrians in the image, and data association, i.e., linking detections across frames to form complete trajectories. For the data association task, approaches typically aim at dev…
Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Technical University of Munich（慕尼黑工业大学）；The University of Hong Kong（香港大学）
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired background distraction. Existing frame-by-frame context learning strategies for repressing background distraction nevertheless lower the tracking speed. Inspired by keyframe-based simultaneous localization and mapping, keyfilter is proposed in visual tracking for the first time, in order to handle the above …
Peng Cheng Laboratory（鹏城实验室）；Dalian University of Technology（大连理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；University of California at Merced（加州大学默塞德分校）
While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference. Since the target state is known in all synthesized data, existing deep trackers can be t…
Lines provide the significantly richer geometric structural information about the environment than points, so lines are widely used in recent Visual Odometry (VO) works. Since VO with lines use line tracking results to locate and map, line tracking is a crucial component in VO. Although the state-of-the-art line tracking methods have made great progress, they are still heavily dependent on line detection or the predicted line segments. In order to relieve the dependencies described above to track line segments completely, accurately, and robustly at higher computational efficiency, we propose …
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shanghai Jiao Tong University（上海交通大学）；Tencent AI Lab（腾讯人工智能实验室）；University of California, Merced（加利福尼亚大学默塞德分校）；Google Cloud AI（谷歌云人工智能）
Existing deep trackers mainly use convolutional neural networks pre-trained for generic object recognition task for representations. Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition. The key issue is that in visual tracking the targets of interest can be arbitrary object class with arbitrary forms. As such, pre-trained deep features are less effective in modeling these targets of arbitrary forms for distinguishing them from the background. In this paper, we pr…
In many visual systems, visual tracking often bases on RGB image sequences, in which some targets are invalid in low-light conditions, and tracking performance is thus affected significantly. Introducing other modalities such as depth and infrared data is an effective way to handle imaging limitations of individual sources, but multi-modal imaging platforms usually require elaborate designs and cannot be applied in many real-world applications at present. Near-infrared (NIR) imaging becomes an essential part of many surveillance cameras, whose imaging is switchable between RGB and NIR based on…
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
Visual object tracking acts as a pivotal component in various emerging video applications. Despite the numerous developments in visual tracking, existing deep trackers are still likely to fail when tracking against objects with dramatic variation. These deep trackers usually do not perform online update or update single sub-branch of the tracking model, for which they cannot adapt to the appearance variation of objects. Efficient updating methods are therefore crucial for tracking while previous meta-updater optimizes trackers directly over parameter space, which is prone to over-fit even coll…
Beihang University（北航）；City University of Macau（澳门城市大学）；Peking University（北京大学）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫 bin Zayed 人工智能大学）；Beijing Normal University（北京师范大学）
UniMergeTrack: Hierarchical Model Merging for General RGB-X Visual Object Tracking
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science, Peking University（北京大学计算机学院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）
Visual object tracking is a fundamental task in the field of computer vision. Recently, Siamese trackers have achieved state-of-the-art performance on recent benchmarks. However, Siamese trackers do not fully utilize semantic and objectness information from pre-trained networks that have been trained on the image classification task. Furthermore, the pre-trained Siamese architecture is sparsely activated by the category label which leads to unnecessary calculations and overfitting. In this paper, we propose to learn a Domain-Aware, that is fully utilizing semantic and objectness information wh…
A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking
Faculty of computer and information science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Center for Machine Perception, Czech Technical University in Prague（布拉格捷克理工大学机器感知中心）
Transformer-based visual object tracking has been utilized extensively. However, the Transformer structure is lack of enough inductive bias. In addition, only focusing on encoding the global feature does harm to modeling local details, which restricts the capability of tracking in aerial robots. Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder. In the employed encoder, a local-recognition attention and a local element correction network are carefully designed for reducing the global redundant inf…
Concordia University（康考迪亚大学）；Chalmers University of Technology（查尔姆斯理工大学）；University of Arkansas（阿肯色大学）
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions. Indeed, we propose a new real-time Depth Perspective-aware Multiple Object Tracking (DP-MOT) approach to tackle the occlusion problem in MOT. A simple yet efficient Subject-Ordered Depth Estimation (SODE) is first proposed to automatically order the depth positions of detected subjects in a 2D scene in an unsupervised manner. Using the output from SODE, a new Active pseudo-3D Kalman filter, a simple but effective extensio…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；SenseTime Group Limited（商汤集团有限公司）
Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers. In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. To this end, features used in traditional Siamese trackers are analyzed at first. We observe that the imbalanced distributio…
Temple University（天普大学）；HiScene Information Technologies（嗨视信息科技）
Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of object to distinguish it from distractors. Specifically, we utilize recurrent neural network (RNN) to model object structure, and incorporate it into CNN to improve its robustness to similar distrac…
An HMM-based framework for identity-aware long-term multi-object tracking from sparse and uncertain identification: use case on long-term tracking in livestock
Centre de développement du porc du Québec（魁北克猪发展中心）
The University of Sydney（悉尼大学）；Beihang University（北京航空航天大学）；Baidu USA（百度美国）；SenseTime（商汤科技）
Multi-Object Tracking (MOT) is a popular topic in computer vision. However, identity issue, i.e., an object is wrongly associated with another object of a different identity, still remains to be a challenging problem. To address it, switchers, i.e., confusing targets thatmay cause identity issues, should be focused. Based on this motivation,this paper proposes a novel switcher-aware framework for multi-object tracking, which consists of Spatial Conflict Graph model (SCG) and Switcher-Aware Association (SAA). The SCG eliminates spatial switch-ers within one frame by building a conflict graph an…
Multiple object tracking (MOT) is a crucial task in computer vision society. However, most tracking-by-detection MOT methods, with available detected bounding boxes, cannot effectively handle static, slow-moving and fast-moving camera scenarios simultaneously due to ego-motion and frequent occlusion. In this work, we propose a novel tracking framework, called "instance-aware MOT" (IA-MOT), that can track multiple objects in either static or moving cameras by jointly considering the instance-level features and object motions. First, robust appearance features are extracted from a variant of Mas…
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）；Tongji University（同济大学）；New York University（纽约大学）；Tsinghua University（清华大学）
