3D motion tracking is a critical task in many computer vision applications. Existing 3D motion tracking techniques require either a great amount of knowledge on the target object or specific hardware. These requirements discourage the wide spread of commercial applications based on 3D motion tracking. 3D motion tracking systems that require no knowledge on the target object and run on a single low-budget camera require estimations of the object projection features (namely, area and position). In this paper, we define the object projection feature estimation problem and we present a novel 3D motion tracking system that needs no knowledge on the target object and that only requires a single low-budget camera, as installed in most computers and smartphones. Our system estimates, in real time, the three-dimensional position of a non-modeled unmarked object that may be non-rigid, non-convex, partially occluded, self occluded, or motion blurred, given that it is opaque, evenly colored, and enough contrasting with the background in each frame. Our system is also able to determine the most relevant object to track in the screen. Our 3D motion tracking system does not impose hard constraints, therefore it allows a market-wide implementation of applications that use 3D motion tracking.
Tracking Any Point Methods for Markerless 3D Tissue Tracking in Endoscopic Stereo Images
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（医学技术与智能系统研究所，汉堡技术大学）；Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf（耳鼻喉头颈外科与肿瘤学部，汉堡艾本多夫大学医学中心）；Martini-Klinik Prostate Cancer Center, University Medical Center Hamburg-Eppendorf（马尔廷诊所前列腺癌中心，汉堡艾本多夫大学医学中心）；Department of Urology, University Medical Center Hamburg-Eppendorf（泌尿外科，汉堡艾本多夫大学医学中心）
本文提出一种利用2D Tracking Any Point网络结合两个CoTracker模型实现内窥镜立体图像无标记3D组织追踪的新方法，实验验证了其在复杂手术场景中的准确性与潜力。
This paper presents an implementation of a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking. In order to achieve this goal, a subset of two computer vision libraries was ported to the Pocket PC platform. They were also adapted to use fixed point math, with the purpose of improving the overall performance of the routines. The port of these libraries opens up the possibility of having other computer vision tas…
Suture needle localization is necessary for autonomous suturing. Previous approaches in autonomous suturing often relied on fiducial markers rather than markerless detection schemes for localizing a suture needle due to the inconsistency of markerless detections. However, fiducial markers are not practical for real-world applications and can often be occluded from environmental factors in surgery (e.g., blood). Therefore in this work, we present a robust tracking approach for estimating the 6D pose of a suture needle when using inconsistent detections. We define observation models based on sut…
Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
The Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK, Department of Computing, Imperial College London, London, UK Department of Mechanical Engineering, Imperial College London, London, UK Department of Surgery \& Cancer, Imperial College London, London, UK Department of Neurosurgery, IRCCS Humanitas Research Hospital, Rozzano, Lombardy, Italy Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. Stanford University School of Medicine, Stanford University, California, USA
Conventional computer-assisted orthopaedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. Visual tracking has recently been proposed to measure the target anatomy in a markerless and effortless way, but the existing methods fail under real-world occlusion caused by intraoperative interventions. Furthermore, such methods are hardware-specific and not accurate enough for surgical applications. In this paper, we propose a RGB-D sensing-based markerless tracking method that is robust a…
Markerless motion capture is an active research in 3D virtualization. In proposed work we presented a system for markerless motion capture for 3D human character animation, paper presents a survey on motion and skeleton tracking techniques which are developed or are under development. The paper proposed a method to transform the motion of a performer to a 3D human character (model), the 3D human character performs similar movements as that of a performer in real time. In the proposed work, human model data will be captured by Kinect camera, processed data will be applied on 3D human model for …
Bridging the Markerless Tracking Validation Gap: Intraoperative X-ray Workflow Integration
The Hamlyn Centre for Robotic Surgery, Imperial College London（伦敦帝国理工学院哈姆林外科机器人中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健 NHS 信托基金）；Stanford University School of Medicine（斯坦福大学医学院）
Markerless Head Tracking for Accurate and Accessible Neuronavigation
Department of Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学）；Department of Psychiatry & Behavioral Sciences, Duke University（精神病学与行为科学系，杜克大学）；Duke Institute for Brain Sciences（杜克脑科学研究所）
FRAMOS GmbH（FRAMOS有限公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Technische Universität München（慕尼黑工业大学）
Tracking of rotation and translation of medical instruments plays a substantial role in many modern interventions. Traditional external optical tracking systems are often subject to line-of-sight issues, in particular when the region of interest is difficult to access or the procedure allows only for limited rigid body markers. The introduction of inside-out tracking systems aims to overcome these issues. We propose a marker-less tracking system based on visual SLAM to enable tracking of instruments in an interventional scenario. To achieve this goal, we mount a miniature multi-modal (monocula…
University of Konstanz（康斯坦茨大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Technische Universität München（慕尼黑工业大学）；Eötvös Loránd University（厄特沃什·罗兰大学）；Hungarian Academy of Sciences（匈牙利科学院）
Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple angles with accurate 3D annotations, are still scant. Here, we propose a method that uses a motion capture (mo-cap) system to obtain a large amount of annotated data on animal movement and posture (2D and 3D) in a semi-automatic manner. Our me…
This paper presents a fast projection mapping method for moving image content projected onto a markerless planar surface using a low-latency Digital Micromirror Device (DMD) projector. By adopting a closed-loop alignment approach, in which not only the surface texture but also the projected image is tracked by a camera, the proposed method is free from a calibration or position adjustment between the camera and projector. We designed fiducial patterns to be inserted into a fast flapping sequence of binary frames of the DMD projector, which allows the simultaneous tracking of the surface textur…
Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the …
Aiming at automatic, convenient and non-instrusive motion capture, this paper presents a new generation markerless motion capture technique, the FlyCap system, to capture surface motions of moving characters using multiple autonomous flying cameras (autonomous unmanned aerial vehicles(UAV) each integrated with an RGBD video camera). During data capture, three cooperative flying cameras automatically track and follow the moving target who performs large scale motions in a wide space. We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras…
Markerless Tracking-Based Registration for Medical Image Motion Correction
Department Artificial Intelligence in Biomedical Engineering, FAU Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, East Carolina University（通讯科学与障碍系，东卡罗来纳大学）；Department of Otolaryngology–Head and Neck Surgery, Louisiana State University Health Sciences Center（耳鼻喉科与头颈外科系，路易斯安那州立大学健康科学中心）；Department of Radiology, FAU Erlangen-Nürnberg（放射学系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, Louisiana State University（通讯科学与障碍系，路易斯安那州立大学）
Eberhard Karls Universität Tübingen（图宾根大学）；Harvard University（哈佛大学）；Columbia University（哥伦比亚大学）；The Rowland Institute at Harvard, Harvard University（哈佛大学罗兰研究所）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）；Baylor College of Medicine（贝勒医学院）
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerless t…
KITE — Toronto Rehabilitation Institute — University Health Network（凯特大西洋康复研究所——多伦多康复研究所——大学医疗网络）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Orofacial deficits are common in people with Parkinson's disease (PD) and their evolution might represent an important biomarker of disease progression. We are developing an automated system for assessment of orofacial function in PD that can be used in-home or in-clinic and can provide useful and objective clinical information that informs disease management. Our current approach relies on color and depth cameras for the estimation of 3D facial movements. However, depth cameras are not commonly available, might be expensive, and require specialized software for control and data processing. Th…
MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation
Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China（生物医学制造与生命质量工程学院，机械工程学院，上海交通大学，上海，中国）；Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China（医学超声科，上海第六人民医院（隶属于上海交通大学医学院））；Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China（医学机器人研究院，上海交通大学，上海，中国）
VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
BiometricsAI, Universidad Autonoma de Madrid (UAM)（生物识别AI中心，马德里自治大学（UAM））；Biomechanics Lab, Universidad Politecnica de Madrid (UPM)（生物力学实验室，马德里理工大学（UPM））
We present a simple lightweight markerless facial performance capture framework using just a monocular video input that combines Active Appearance Models for feature tracking and prior constraints on 3D shapes into an integrated objective function. 2D monocular inputs inherently lack information along the depth axis and can lead to physically implausible solutions. In order to address this loss of information, we enforce a constraint on our objective function within a probabilistic framework that uses preexisting animations obtained from accurate 3D tracking systems, thus achieving more plausi…
Objective The coordination of human movement directly reflects function of the central nervous system. Small deficits in movement are often the first sign of an underlying neurological problem. The objective of this research is to develop a new end-to-end, deep learning-based system, Rapid-Motion-Track (RMT) that can track the fastest human movement accurately when webcams or laptop cameras are used. Materials and Methods We applied RMT to finger tapping, a well-validated test of motor control that is one of the most challenging human motions to track with computer vision due to the small keyp…
University of Toronto（多伦多大学）；University of British Columbia（不列颠哥伦比亚大学）；York University（约克大学）
We propose a generative approach to physics-based motion capture. Unlike prior attempts to incorporate physics into tracking that assume the subject and scene geometry are calibrated and known a priori, our approach is automatic and online. This distinction is important since calibration of the environment is often difficult, especially for motions with props, uneven surfaces, or outdoor scenes. The use of physics in this context provides a natural framework to reason about contact and the plausibility of recovered motions. We propose a fast data-driven parametric body model, based on linear-b…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Amsterdam（阿姆斯特丹大学）
Humans constantly interact with daily objects to accomplish tasks. To understand such interactions, computers need to reconstruct these from cameras observing whole-body interaction with scenes. This is challenging due to occlusion between the body and objects, motion blur, depth/scale ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community focuses either on interacting hands, ignoring the body, or on interacting bodies, ignoring hands. The GRAB dataset addresses dexterous whole-body interaction but uses marker-based MoCap and…
Shirley Ryan AbilityLab（雪莉·瑞安能力实验室）；Northwestern University（西北大学）
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to recons…
Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements
Biomedical Engineering Department, Northwestern University（西北大学生物医学工程系）；Shirley Ryan AbilityLab（谢里尔·瑞安能力实验室）；Department of Organismal Biology and Anatomy, University of Chicago（芝加哥大学器官生物学与解剖学系）；Department of Physical Medicine and Rehabilitation, Northwestern University（西北大学物理医学与康复系）
Predictive Multi-Landmark OCT Tracking for Increased Motion Robustness
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医学技术与智能系统研究所）；Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心耳鼻咽喉头颈外科与肿瘤科）
Gokhale Method Institute（戈卡莱方法研究所）；University of Bonn（波恩大学）
We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighbourhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to …
KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture
Department of PM&R Northwestern University Shirley Ryan AbilityLab（运动医学系西北大学施里尔 Ryan 能力实验室）
We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures. An initial model is generated by locally stitching together models of the individual parts of the human body, which we refer to as the "Frankenstein" model. This model enables the full expression of part movements, including face and hands by a single seamless model. Using a large-scale capture of people wearing everyday clothes, we optimize the Frankenstein model to create "Adam". Adam is a calibrated model that shares the same s…
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications. This work instead develops a novel unsupervised domain adaptation framework for nighttime aerial tracking (named UDAT). Specifically, a unique object discovery approach is provided to generate training patches from raw nighttime tracking videos. To tackle the domain discrepancy, we employ a Transformer-based bridging layer post to the feature extractor to align image features fro…
University of Southern California（南加州大学）；Army Research Laboratory（陆军研究实验室）
Supervised and unsupervised deep trackers that rely on deep learning technologies are popular in recent years. Yet, they demand high computational complexity and a high memory cost. A green unsupervised single-object tracker, called GUSOT, that aims at object tracking for long videos under a resource-constrained environment is proposed in this work. Built upon a baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT contains two additional new modules: 1) lost object recovery, and 2) color-saliency-based shape proposal. They help resolve the tracking loss problem and offe…
Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternativ…
A Novel Automatic Real-time Motion Tracking Method in MRI-guided Radiotherapy Using Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家癌症临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院）
University of Science and Technology of China（中国科学技术大学）；Tencent AI Lab（腾讯人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）
We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhil…
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Tencent AI Lab（腾讯人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）
The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotations and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. Specifically, the tracker is able to forward localize a target object in successive frames and backtrace to its initial position in the first fr…
University of Science and Technology of China（中国科学技术大学）；Microsoft Cloud AI（微软云人工智能）；International Digital Economy Academy（国际数字经济研究院）
Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The common practice in multi-object tracking is re-identifying the missed objects after their reappearance. Though tracking performance can be boosted by the re-identification, the annotation of identity is required to train the model. In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector. In this paper, we focus on online multi-object tracki…
In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework. Since our tracker can work with detections coming either from unsupervised and supervised object detectors, we also investigated the impact of supervised and unsupervised detection inputs in our method and for tracking road users in general. We also compared our results with existing methods that were applied on the UA-Detrac and the UrbanTracker datase…
Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance. In contrast, the Lucas-Kanade (LK) algorithm works well without any annotation. However, LK has a strong assumption of photometric (brightness) consistency on image intensity and is easy to drift because of large motion, occlusion, and aperture problem. To relax the assumption and alleviate the drift problem, we propose CyLKs, a data-driven way of training Lucas-Kanade in an unsupervised manner. CyLKs learns a feature transformation through CNNs, transforming t…
Fraunhofer Center Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；Institute for Machine Learning and Analytics (IMLA) Offenburg University（奥芬堡大学机器学习与分析研究所（IMLA））；University of Mannheim（曼海姆大学）
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance i…
University of Southern California（南加州大学）；Army Research Laboratory（美国陆军研究实验室）
An unsupervised, lightweight and high-performance single object tracker, called UHP-SOT, was proposed by Zhou et al. recently. As an extension, we present an enhanced version and name it UHP-SOT++ in this work. Built upon the foundation of the discriminative-correlation-filters-based (DCF-based) tracker, two new ingredients are introduced in UHP-SOT and UHP-SOT++: 1) background motion modeling and 2) object box trajectory modeling. The main difference between UHP-SOT and UHP-SOT++ is the fusion strategy of proposals from three models (i.e., DCF, background motion and object box trajectory mode…
Technische Universität München（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心）
The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter, within which a novel deterministic resampling strategy (DRS) is applied. The proposed system is evaluated quantitatively with the ground truth data measured by an inertial sensor system. In addition…
NYU Multimedia and Visual Computing Lab（纽约大学多媒体与视觉计算实验室）；New York University Abu Dhabi（纽约大学阿布扎比分校）；New York University（纽约大学）
This paper deals with the problem of 3D tracking, i.e., to find dense correspondences in a sequence of time-varying 3D shapes. Despite deep learning approaches have achieved promising performance for pairwise dense 3D shapes matching, it is a great challenge to generalize those approaches for the tracking of 3D time-varying geometries. In this paper, we aim at handling the problem of 3D tracking, which provides the tracking of the consecutive frames of 3D shapes. We propose a novel unsupervised 3D shape registration framework named DeepTracking-Net, which uses the deep neural networks (DNNs) a…
3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing reliable object projection feature estimation techniques are based on ray-casting or grid-filling from the inner point. These techniques assume the edge image to be accurate. However, in real case scenarios, e…
Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications. Here we propose to use the feature learning pipeline for visual tracking. Tracking is implemented using tracking-by-detection and the resulted framework is very simple yet effective. First, online dictionary learning is used to build a dictionary, which captures the appearance changes of the tracking target as well as the background changes. Given a test image window, we extr…
Psychological studies have found that human visual tracking system involves learning, memory, and planning. Despite recent successes, not many works have focused on memory and planning in deep learning based tracking. We are thus interested in memory augmented network, where an external memory remembers the evolving appearance of the target (foreground) object without backpropagation for updating weights. Our Dual Augmented Memory Network (DAWN) is unique in remembering both target and background, and using an improved attention LSTM memory to guide the focus on memorized features. DAWN is eff…
Rutgers University（罗格斯大学）；New York University School of Medicine（纽约大学医学院）；NVIDIA（英伟达）；SenseBrain；Shanghai AI Laboratory（上海人工智能实验室）
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty of motion tracking encountered with t-MRI images. In this paper, we propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. We first estimate the motion field (INF) between any two consecutive t-MRI frames by a bi-directional generative diffeomorphic registration neural network. Using this result, we then e…
IMT Atlantique（IMT大西洋高等矿业电信学院）；LaTIM UMR 1101, Inserm（LaTIM（INSERM UMR 1101））；ICube UMR 7357, CNRS, Université de Strasbourg（斯特拉斯堡大学CNRS ICube（UMR 7357））；Université de Bretagne Occidentale（布列塔尼西部大学）
Finding correspondences between structural entities decomposing images is of high interest for computer vision applications. In particular, we analyze how to accurately track superpixels - visual primitives generated by aggregating adjacent pixels sharing similar characteristics - over extended time periods relying on unsupervised learning and temporal integration. A two-step video processing pipeline dedicated to long-term superpixel tracking is proposed. First, unsupervised learning-based superpixel matching provides correspondences between consecutive and distant frames using new context-ri…
We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any input about which objects should be tracked. Towards solving this task, we present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking) as a simple and generic algorithm which is able to track and segment a large variety of objects. This algorithm builds up tracks in a number stages, first grouping segments into short tracklets that are spatio-temporally c…
