This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking. Results of human detection and tracking from two cameras are integrated to obtain the 3D information. OpenPose is used for human detection. In the case of a general processing a stereo camera, a range image of the entire scene is acquired as precisely as possible, and then the range image is processed. However, there are problems such as incorrect matching and computational cost for the calibration process. A new stereo vision framework is proposed to cope with the problems. The effectiveness of the proposed framework and the method is verified through target-tracking experiments.
Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
In the field of navigation and visual servo, it is common to calculate relative pose by feature points on markers, so keeping markers in camera's view is an important problem. In this paper, we propose a novel approach to calculate field-of-view (FOV) constraint of markers for camera. Our method can make the camera maintain the visibility of all feature points during the motion of mobile robot. According to the angular aperture of camera, the mobile robot can obtain the FOV constraint region where the camera cannot keep all feature points in an image. Based on the FOV constraint region, the mo…
The field of robotic vision is developing rapidly. Robots can react intelligently and provide assistance to user activities through sentient computing. Since industrial applications pose complex requirements that cannot be handled by humans, an efficient low cost and robust technique is required for the tracking of mobile industrial robots. The existing sensor based techniques for mobile robot tracking are expensive and complex to deploy, configure and maintain. Also some of them demand dedicated and often expensive hardware. This paper presents a low cost vision based technique called Hybrid …
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能与智能交通研究部）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进科技学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
We present an approach for estimating a mobile robot's pose w.r.t. the allocentric coordinates of a network of static cameras using multi-view RGB images. The images are processed online, locally on smart edge sensors by deep neural networks to detect the robot and estimate 2D keypoints defined at distinctive positions of the 3D robot model. Robot keypoint detections are synchronized and fused on a central backend, where the robot's pose is estimated via multi-view minimization of reprojection errors. Through the pose estimation from external cameras, the robot's localization can be initialize…
Peking University（北京大学）；Beijing Film Academy（北京电影学院）；Shanghai Jiao Tong University（上海交通大学）；Deepwise AI Lab（深睿人工智能实验室）
Active Object Tracking (AOT) is crucial to many visionbased applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e.g., target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for act…
In this paper, a novel dual-mode robust model predictive control (MPC) approach is proposed for solving the tracking control problem of non-holonomoic mobile robots with additive bounded disturbance. To reduce the negative effect of disturbance and drive the state of real system closer to the one of nominal system , a robust reference signal is introduced into the cost function of MPC. In order to reduced the computation burden caused by online optimization of MPC and further improve the tracking accuracy, a dual-mode control strucuture consisting of the robust MPC and the local nonlinear robu…
National Cheng Kung University（成功大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
This paper investigates the resilient control, analysis, recovery, and operation of mobile robot networks in time-varying formation tracking under deception attacks on global positioning. Local and global tracking control algorithms are presented to ensure redundancy of the mobile robot network and to retain the desired functionality for better resilience. Lyapunov stability analysis is utilized to show the boundedness of the formation tracking error and the stability of the network under various attack modes. A performance index is designed to compare the efficiency of the proposed formation …
Massachusetts Institute of Technology（麻省理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Theref…
This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filte…
VNUHCM-University of Science（越南胡志明市国家大学胡志明市自然科学大学）
This study develops a robot mobility policy based on deep reinforcement learning. Since traditional methods of conventional robotic navigation depend on accurate map reproduction as well as require high-end sensors, learning-based methods are positive trends, especially deep reinforcement learning. The problem is modeled in the form of a Markov Decision Process (MDP) with the agent being a mobile robot. Its state of view is obtained by the input sensors such as laser findings or cameras and the purpose is navigating to the goal without any collision. There have been many deep learning methods …
Slip is a very common phenomena present in wheeled mobile robotic systems. It has undesirable consequences such as wasting energy and impeding system stability. To tackle the challenge of mobile robot trajectory tracking under slippery conditions, we propose a hierarchical framework that learns and adapts gains of the tracking controllers simultaneously online. Concretely, a reinforcement learning (RL) module is used to auto-tune parameters in a lateral predictive controller and a longitudinal speed PID controller. Experiments show the necessity of simultaneous gain tuning, and have demonstrat…
Precision landing is a remaining challenge in autonomous drone flight. Fiducial markers provide a computationally cheap way for a drone to locate a landing pad and autonomously execute precision landings. However, most work in this field depends on either rigidly-mounted or downward-facing cameras which restrict the drone's ability to detect the marker. We present a method of autonomous landing that uses a gimbal-mounted camera to quickly search for the landing pad by simply spinning in place while tilting the camera up and down, and to continually aim the camera at the landing pad during appr…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Univrses AB（Univrses AB公司）
We consider the problem of tracking the 6D pose of a moving RGB-D camera in a neural scene representation. Different such representations have recently emerged, and we investigate the suitability of them for the task of camera tracking. In particular, we propose to track an RGB-D camera using a signed distance field-based representation and show that compared to density-based representations, tracking can be sped up, which enables more robust and accurate pose estimates when computation time is limited.
Adobe Research（奥多比研究院）；KAIST（韩国科学技术院）；Hyundai Motor Group（现代汽车集团）
We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing algorithms rely heavily on low-level image features that are based mainly on local gradient information, we consider that gradient quantity can determine the proper exposure level, allowing a camera to capture the important image features in a manner robust to illumination conditions. We then extend this concept to a multi-camera system and present a new control algorithm to achieve both brightness consistency between…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；National Laboratory of Pattern Recognition（模式识别国家重点实验室）
Non-overlapping multi-camera visual object tracking typically consists of two steps: single camera object tracking and inter-camera object tracking. Most of tracking methods focus on single camera object tracking, which happens in the same scene, while for real surveillance scenes, inter-camera object tracking is needed and single camera tracking methods can not work effectively. In this paper, we try to improve the overall multi-camera object tracking performance by a global graph model with an improved similarity metric. Our method treats the similarities of single camera tracking and inter-…
DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；School of Mechanical Engineering, Xi’an Jiaotong University（西安交通大学机械工程学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
University of Chinese Academy of Sciences（中国科学院大学）
Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges. By analyzing the corresponding characteristics and disadvantages of the existing algorithms, problems in multi-camera tracking are summarized and some new directions for future work are also generalized.
The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations' complementary strengths to develop an enhanced version. The proposed tracker consists of a general strategy to evaluate the optimization problem's degeneracy online and then switch proper representations. Both TS and EM are motion- and scene-dependent, and thus it is important to figure out their limitations in tracking. We develop six tracker variations and conduct a thorough comp…
We present Mono-STAR, the first real-time 3D reconstruction system that simultaneously supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological change under a unified framework. The proposed system solves a new optimization problem incorporating optical-flow-based 2D constraints to deal with fast motion and a novel semantic-aware deformation graph (SAD-graph) for handling topology change. We test the proposed system under various challenging scenes and demonstrate that it significantly outperforms existing state-of-the-art methods.
Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. These features, along with a very low power consumption, make event cameras an ideal complement to standard cameras for VR/AR and video game applications. With these applications in mind, this paper tackles the problem of accurate, low-latency tracking of an…
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；Universidad UTE（UTE大学）
Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. …
Gwangju Institute of Science and Technology（光州科学技术院）
In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Each status represents three different stages of a multi-camera track: tracking, searching, and end-of-track. The tracking status means…
One of fundamental issues for security robots is to detect and track people in the surroundings. The main problems of this task are real-time constraints, a changing background, varying illumination conditions and a non-rigid shape of the person to be tracked. In this paper, we propose a solution for tracking with a pan-tilt camera and a passive infrared range (PIR) sensor to detect the moving object based on consecutive frame difference. The proposed method is excellent in real-time performance because it requires only a little memory and computation. Experiment results show that this method …
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated multi-camera tracking benchmark which makes great progress in this field. This report is dedicated to briefly introduce our method on DukeMTMC and show that simple hierarchical clustering with well-trained person re-identification features can get good results on this dataset.
Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout because monocular multi-object tracking (MOT) systems often fail in cluttered and crowded environments due to occlusion. Multiple highly overlapped cameras can significantly alleviate the problem by recovering partial 3D information. However, the cost of creating a high-quality multi-camera tracking dataset with diverse camera settings and backgrounds has limited the dataset scale in this domain. In this paper, we provide a large-scale densely-labeled mu…
Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding bo…
Australian National University（澳大利亚国立大学）；Tsinghua University（清华大学）
Multi-target multi-camera tracking (MTMCT) systems track targets across cameras. Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood. In single camera tracking, local neighborhood refers to consecutive frames; in multi-camera tracking, it refers to neighboring cameras that the target may appear successively. For similarity estimation, tracking systems often adopt appearance features learned from the re-identification (re-ID) perspective. Different from tracking, re-ID usually does not have access to the trajectory c…
Event cameras are a paradigm shift in camera technology. Instead of full frames, the sensor captures a sparse set of events caused by intensity changes. Since only the changes are transferred, those cameras are able to capture quick movements of objects in the scene or of the camera itself. In this work we propose a novel method to perform camera tracking of event cameras in a panoramic setting with three degrees of freedom. We propose a direct camera tracking formulation, similar to state-of-the-art in visual odometry. We show that the minimal information needed for simultaneous tracking and …
University IUAV of Venice（威尼斯IUAV大学）；Ca’ Foscari University of Venice（威尼斯大学）；University of Parma（帕尔马大学）；University of Central Florida（中佛罗里达大学）
In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to sol…
In this paper, we propose a pipeline for multi-target visual tracking under multi-camera system. For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking. However, most of the multi-camera tracking algorithms emphasis on single camera across frame data association. Thus in our work, we model our tracking problem as a global graph, and adopt Generalized Maximum Multi Clique optimization problem as our core algorithm to take both across frame and across camera data correlatio…
This letter focuses on the task of Multi-Target Multi-Camera vehicle tracking. We propose to associate single-camera trajectories into multi-camera global trajectories by training a Graph Convolutional Network. Our approach simultaneously processes all cameras providing a global solution, and it is also robust to large cameras unsynchronizations. Furthermore, we design a new loss function to deal with class imbalance. Our proposal outperforms the related work showing better generalization and without requiring ad-hoc manual annotations or thresholds, unlike compared approaches.
Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is p…
Indraprastha Institute of Information Technology (IIIT-Delhi)（英德拉普拉萨信息技术学院（IIIT-德里））
Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data, inefficient schemes for making re-identification or trajectory association queries can incur prohibitively large computational requirements. In this paper, we address the problem of intelligent sche…
Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over $95K$ bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone …
Technische Universität Berlin（柏林工业大学）；Einstein Center Digital Future（爱因斯坦数字未来中心）；SCIoI Excellence Cluster（SCIoI卓越集群）
This work introduces a co-capture system for multi-animal visual data acquisition using conventional cameras and event cameras. Event cameras offer multiple advantages over frame-based cameras, such as a high temporal resolution and temporal redundancy suppression, which enable us to efficiently capture the fast and erratic movements of fish. We furthermore present an event-based multi-animal tracking algorithm, which proves the feasibility of the approach and sets the baseline for further exploration of combining the advantages of event cameras and conventional cameras for multi-animal tracki…
Tracking a crowd in 3D using multiple RGB cameras is a challenging task. Most previous multi-camera tracking algorithms are designed for offline setting and have high computational complexity. Robust real-time multi-camera 3D tracking is still an unsolved problem. In this work, we propose a novel end-to-end tracking pipeline, Deep Multi-Camera Tracking (DMCT), which achieves reliable real-time multi-camera people tracking. Our DMCT consists of 1) a fast and novel perspective-aware Deep GroudPoint Network, 2) a fusion procedure for ground-plane occupancy heatmap estimation, 3) a novel Deep Glim…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Increasing demand for meat products combined with farm labor shortages has resulted in a need to develop new real-time solutions to monitor animals effectively. Significant progress has been made in continuously locating individual pigs using tracking-by-detection methods. However, these methods fail for oblong pens because a single fixed camera does not cover the entire floor at adequate resolution. We address this problem by using multiple cameras, placed such that the visual fields of adjacent cameras overlap, and together they span the entire floor. Avoiding breaks in tracking requires int…
ETH Zürich（苏黎世联邦理工学院）；National Tsing Hua University（台湾清华大学）
To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusion in a multi-camera setup. In this paper, we propose a method for panoramic 3D object tracking, called CC-3DT, that associates and models object trajectories both temporally and across views, and improves the overall tracking consistency. In particular, our method fuses 3D detec…
Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model
School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）；Institute of Integrated Science and Technology, Hosei University（法政大学综合科学技术研究所）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）
Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets (e.g. human), static cameras, and/or camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation i…
We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle tracking from traffic video sequences. Cross-camera vehicle tracking has proved to be a challenging task due to perspective, scale and speed variance, as well occlusions and noise conditions. Our method is based on a modular approach that first detects vehicles frame-by-frame using Faster R-CNN, then tracks detections through single camera using Kalman filter, and finally matches tracks by a triplet metric learning strategy. We conduct experiments on TrackNet within the AI City Challenge framework, and present competiti…
Universidad de Las Palmas de Gran Canaria（拉斯帕尔马斯大学）；Università Parthenope di Napoli（那不勒斯帕萨诺普大学）；Università degli Studi di Cagliari（卡利亚里大学）
In the present paper, we propose a source camera identification method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture wh…
University of Arkansas（阿肯色大学）；Concordia University（康考迪亚大学）；University of Wollongong（伍伦贡大学）
The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car. It is essential for object detection and tracking to address these new challenges in multi-camera settings. In order to address these challenges, this work introduces novel Single-Stage Global Association Tracking approaches to associate one or more detection from multi-cameras with tracked objects. These approaches aim to solve fragment-tracking issues caused by inconsistent 3D object detection. Moreover, our models also i…
Object tracking and localization is a complex task that typically requires processing power beyond the capabilities of low-power embedded cameras. This paper presents a new approach to real-time object tracking and localization using multi-view binary keypoints descriptor. The proposed approach offers a compromise between processing power, accuracy and networking bandwidth and has been tested using multiple distributed low-power smart cameras. Additionally, multiple optimization techniques are presented to improve the performance of the keypoints descriptor for low-power embedded systems.
University of Washington（华盛顿大学）；Electronics and Telecommunications Research Institute（电子通信研究院）
In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM). Given a video sequence and the corresponding frame-by-frame vehicle detections, we first address the isolated tracklets issue from single camera tracking (SCT) by the proposed traffic-aware single-camera tracking (TSCT). Then, after automatically constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated from camera topological configuration to obtain the spatial and tempora…
University of Washington（华盛顿大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Microsoft Research（微软研究院）
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement. The current state-of-the-art neural models are typically trained in a supervised manner using videos accompanied by gold standard physiological measurements. However, they often generalize poorly out-of-domain examples (i.e., videos that are unlike those in the training set). Personalizing models can help improve model generalizability, but many personalization techniques still require some gold standard data. To help alleviate this dependency, in this paper, we presen…
Tracking multiple people across multiple cameras is an open problem. It is typically divided into two tasks: (i) single-camera tracking (SCT) - identify trajectories in the same scene, and (ii) inter-camera tracking (ICT) - identify trajectories across cameras for real surveillance scenes. Many methods cater to SCT, while ICT still remains a challenge. In this paper, we propose a tracking method which uses motion cues and a feature aggregation network for template-based person re-identification by incorporating metadata such as person bounding box and camera information. We present a feature a…
The University of Adelaide（阿德莱德大学）；University of Queensland（昆士兰大学）；Western Sydney University（西悉尼大学）
Star trackers are primarily optical devices that are used to estimate the attitude of a spacecraft by recognising and tracking star patterns. Currently, most star trackers use conventional optical sensors. In this application paper, we propose the usage of event sensors for star tracking. There are potentially two benefits of using event sensors for star tracking: lower power consumption and higher operating speeds. Our main contribution is to formulate an algorithmic pipeline for star tracking from event data that includes novel formulations of rotation averaging and bundle adjustment. In add…
Lighting understanding plays an important role in virtual object composition, including mobile augmented reality (AR) applications. Prior work often targets recovering lighting from the physical environment to support photorealistic AR rendering. Because the common workflow is to use a back-facing camera to capture the physical world for overlaying virtual objects, we refer to this usage pattern as back-facing AR. However, existing methods often fall short in supporting emerging front-facing mobile AR applications, e.g., virtual try-on where a user leverages a front-facing camera to explore th…
State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences（中国科学院软件研究所计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Machine Intelligence Technology Lab Alibaba Group（阿里巴巴集团机器智能技术实验室）
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDETracker (without detection module) to track single-camera vehicles and generate single-came…
Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC)（科尔多瓦迈蒙尼德生物医学研究所）；Universidad de Córdoba（科尔多瓦大学）
Object tracking is a key aspect in many applications such as augmented reality in medicine (e.g. tracking a surgical instrument) or robotics. Squared planar markers have become popular tools for tracking since their pose can be estimated from their four corners. While using a single marker and a single camera limits the working area considerably, using multiple markers attached to an object requires estimating their relative position, which is not trivial, for high accuracy tracking. Likewise, using multiple cameras requires estimating their extrinsic parameters, also a tedious process that mu…
