Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. In this paper, we present a new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories.
We study the use of overlapping and non-overlapping camera layouts in estimating the ego-motion of a moving robot. To estimate the location and orientation of the robot, we investigate using four cameras as non-overlapping individuals, and as two stereo pairs. The pros and cons of the two approaches are elucidated. The cameras work independently and can have larger field of view in the non-overlapping layout. However, a scale factor ambiguity should be dealt with. On the other hand, stereo systems provide more accuracy but require establishing feature correspondence with more computational dem…
Viterbi Faculty of Electrical Engineering（维特比电气工程学院）；Technion - Israel Institute of Technology（以色列理工学院）；Norman & Helen Asher Space Research Institute（诺曼与海伦·阿舍空间研究所）；Zentrum für Telematik e.V.（远程信息处理中心协会）
Target tracking in a camera network is an important task for surveillance and scene understanding. The task is challenging due to disjoint views and illumination variation in different cameras. In this direction, many graph-based methods were proposed using appearance-based features. However, the appearance information fades with high illumination variation in the different camera FOVs. We, in this paper, use spatial and temporal information as the state of the target to learn a policy that predicts the next camera given the current state. The policy is trained using Q-learning and it does not…
We propose an easy-to-use non-overlapping camera calibration method. First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames. Next, the pose between cameras are estimated. Instead of using a batch method, we propose an on-line method of the inter-camera pose estimation. Furthermore, we implement the entire procedure on a computation graph. Experiments with simulations and the KITTI dataset show the proposed method to be effective in simulation.
Surveillance cameras are widely applied for indoor occupancy measurement and human movement perception, which benefit for building energy management and social security. To address the challenges of limited view angle of single camera as well as lacking of inter-camera collaboration, this study presents a non-overlapping multi-camera system to enlarge the surveillance area and devotes to retrieve the same person appeared from different camera views. The system is deployed in an office building and four-day videos are collected. By training a deep convolutional neural network, the proposed syst…
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…
Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
USDA-ARS-AFRS（美国农业部农业研究服务局农业和食品研究处）；University of Florida（佛罗里达大学）；University of California-Davis（加州大学戴维斯分校）；Embrapa Digital Agriculture（巴西农业研究公司数字农业研究所）
Beijing University of Aeronautics and Astronautics（北京航空航天大学）
One of the fundamental requirements for visual surveillance using non-overlapping camera networks is the correct labeling of tracked objects on each camera in a consistent way,in the sense that the captured tracklets, or observations in this paper, of the same object at different cameras should be assigned with the same label. In this paper, we formulate this task as a Bayesian inference problem and propose a distributed inference framework in which the posterior distribution of labeling variable corresponding to each observation, conditioned on all history appearance and spatio-temporal evide…
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen DJI Sciences and Technologies Ltd.（深圳市大疆创新科技有限公司）；Tsinghua University（清华大学）
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estimation. The former requires cameras to have large overlapping areas and sufficient baseline between cameras, while the latter that processes each image independently can hardly guarantee the structure consistency between cameras. In this paper, we propose a novel multi-camera collaborative depth prediction method that does not require large overlapping areas while maintaining structure consistency between cameras. Spec…
Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction
Research in Orthopedic Computer Science（骨科计算机科学研究）；University Hospital Balgrist（巴尔格斯特大学医院）；University of Zurich（苏黎世大学）；Computer Vision and Geometry（计算机视觉与几何）；ETH Zurich（苏黎世联邦理工学院）
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；US Army Research Lab（美国陆军研究实验室）；West Virginia University（西弗吉尼亚大学）
We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks. In this setting cameras are strategically placed such that less robust sensors, e.g. geophones that monitor seismic activity, are located within the field of views (FOVs) of cameras. The primary challenge is being able to leverage sufficient information from videos where there are less than 40 pixels on targets, while also taking advantage of less discriminative information from other modalities, e.g. seismic. Unlike state-of-the-art …
Federal University of Technology - Paran\' a (UTFPR), Curitiba, Brazil
We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification. The proposed network is fed simultaneously with small coarse patches of the vehicle shape's, with 96 x 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 x 48 pixels, in the other one. Then, we combined the strengths of both streams by merging the Siamese distance descriptors with a sequence of fully connected layers, as an attempt to tackle a major problem in the field, false alarms caused by a huge number of car design and models with …
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Mechanical and Power Engineering, East China University of Science and Technology（华东理工大学机械与动力工程学院）
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…
This paper presents a framework for the targetless extrinsic calibration of stereo cameras and Light Detection and Ranging (LiDAR) sensors with a non-overlapping Field of View (FOV). In order to solve the extrinsic calibrations problem under such challenging configuration, the proposed solution exploits road markings as static and robust features among the various dynamic objects that are present in urban environment. First, this study utilizes road markings that are commonly captured by the two sensor modalities to select informative images for estimating the extrinsic parameters. In order to…
Sun Yat-Sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that source domain and target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domai…
An analysis of the relative motion and point feature model configurations leading to solution degeneracy is presented, for the case of a Simultaneous Localization and Mapping system using multicamera clusters with non-overlapping fields-of-view. The SLAM optimization system seeks to minimize image space reprojection error and is formulated for a cluster containing any number of component cameras, observing any number of point features over two keyframes. The measurement Jacobian is transformed to expose a reduced-dimension representation such that the degeneracy of the system can be determined…
G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity
Wuhan University（武汉大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Wuhan AI Research（武汉人工智能研究院）；Rongyun Robot (Guizhou) Co., Ltd.（融云机器人（贵州）有限公司）
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-…
MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects
Human-Centered-Computing and Extended Reality Lab（以人为本计算与扩展现实实验室）；Klinikum rechts der Isar（右岸诊所）；Orthopedics and Sports Orthopedics（骨科和运动骨科）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；TH Köln（科隆大学）；Computer Graphics Group（计算机图形学组）；Photogrammetry and Remote Sensing Lab（摄影测量与遥感实验室）
A panoramic image mosaic is an attractive visualization for viewing many overlapping photos, but its images must be both captured and processed correctly to produce an acceptable composite. We propose Swipe Mosaics, an interactive visualization that places the individual video frames on a 2D planar map that represents the layout of the physical scene. Compared to traditional panoramic mosaics, our capture is easier because the user can both translate the camera center and film moving subjects. Processing and display degrade gracefully if the footage lacks distinct, overlapping, non-repeating t…
Multi-view frame reconstruction is an important problem particularly when multiple frames are missing and past and future frames within the camera are far apart from the missing ones. Realistic coherent frames can still be reconstructed using corresponding frames from other overlapping cameras. We propose an adversarial approach to learn the spatio-temporal representation of the missing frame using conditional Generative Adversarial Network (cGAN). The conditional input to each cGAN is the preceding or following frames within the camera or the corresponding frames in other overlapping cameras,…
University of Warwick（华威大学）；Nanyang Technological University（南洋理工大学）
We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian traj…
University of Texas – Arlington（德克萨斯大学阿灵顿分校）；Samsung Research America（三星美国研究院）
Dual-fisheye lens cameras have been increasingly used for 360-degree immersive imaging. However, the limited overlapping field of views and misalignment between the two lenses give rise to visible discontinuities in the stitching boundaries. This paper introduces a novel method for dual-fisheye camera stitching that adaptively minimizes the discontinuities in the overlapping regions to generate full spherical 360-degree images. Results show that this approach can produce good quality stitched images for Samsung Gear 360 -- a dual-fisheye camera, even with hard-to-stitch objects in the stitchin…
Queensland University of Technology（昆士兰科技大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Stanford University（斯坦福大学）
This paper proposes the design of a custom mirror-based light field camera adapter that is cheap, simple in construction, and accessible. Mirrors of different shape and orientation reflect the scene into an upwards-facing camera to create an array of virtual cameras with overlapping field of view at specified depths, and deliver video frame rate light fields. We describe the design, construction, decoding and calibration processes of our mirror-based light field camera adapter in preparation for an open-source release to benefit the robotic vision community.
Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras
Gwangju Institute of Science and Technology（光州科学技术院）；Intel Visual Computing Institute（英特尔视觉计算研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；ASRI（高级软件与机器人研究所）；Seoul National University（首尔大学）
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic
COMSATS University Islamabad, Lahore Campus（COMSATS大学伊斯兰堡分校拉合尔校区）；Pakistan Navy Engineering College, National University of Sciences and Technology (NUST)（巴基斯坦国立科技大学巴基斯坦海军工程学院）
提出一种基于深度学习的多目标多摄像头跟踪框架，利用Mask R-CNN、迁移学习与Deep SORT等技术解决城市规模交通中的车辆跟踪难题，在AI City Challenge数据集上取得IDF1为0.8289的优异性能。
New York University（纽约大学）；Microsoft Research（微软研究院）
This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks. MCSSL associates bounding boxes between cameras with overlapping fields of view by leveraging epipolar geometry and state-of-the-art tracking and reID algorithms, and prudently generates two sets of pseudo-labels to fine-tune backbone and detection networks respectively in an object detection model. To train effectively on pseudo-labels,a powerful reID-like pretext task with consistency loss is constructed for model customization. Our evaluation shows that compar…
Concordia University（康考迪亚大学）；VinAI Research（VinAI研究院）；Chalmers University of Technology（查尔姆斯理工大学）；University of Arkansas（阿肯色大学）；University of Science VNU-HCM（越南国立大学胡志明市科学大学）
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, pr…
University of Adelaide（阿德莱德大学）；Centre for Automotive Safety Research（汽车安全研究中心）
Accurate 7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. In principle, this could be achieved by a single camera system that is capable of detecting the pose of each vehicle but this would require a large, accurately labelled dataset from which to train the detector. Although large vehicle pose datasets exist (ostensibly developed for autonomous vehicles), we find training on these datasets inadequate. These datasets contain images from a ground level viewpoint, whereas an ideal view for intersection observation would be…
3D multi-object tracking is a crucial component in the perception system of autonomous driving vehicles. Tracking all dynamic objects around the vehicle is essential for tasks such as obstacle avoidance and path planning. Autonomous vehicles are usually equipped with different sensor modalities to improve accuracy and reliability. While sensor fusion has been widely used in object detection networks in recent years, most existing multi-object tracking algorithms either rely on a single input modality, or do not fully exploit the information provided by multiple sensing modalities. In this work…
Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In additio…
Activity detection is one of the attractive computer vision tasks to exploit the video streams captured by widely installed cameras. Although achieving impressive performance, conventional activity detection algorithms are usually designed under certain constraints, such as using trimmed and/or object-centered video clips as inputs. Therefore, they failed to deal with the multi-scale multi-instance cases in real-world unconstrained video streams, which are untrimmed and have large field-of-views. Real-time requirements for streaming analysis also mark brute force expansion of them unfeasible. …
Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera, too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd. Therefore, to solve the wide-area counting task r…
Northwestern Polytechnical University（西北工业大学）；Australian National University（澳大利亚国立大学）；Australia Centre of Excellence for Robotic Vision (ACRV)（澳大利亚机器人视觉卓越中心）
This paper studies the relative pose problem for autonomous vehicle driving in highly dynamic and possibly cluttered environments. This is a challenging scenario due to the existence of multiple, large, and independently moving objects in the environment, which often leads to excessive portion of outliers and results in erroneous motion estimation. Existing algorithms cannot cope with such situations well. This paper proposes a new algorithm for relative pose using a multi-camera system with multiple non-overlapping individual cameras. The method works robustly even when the numbers of outlier…
