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MULTI-CAMERA POSITIONING FOR AUTOMATED TRACKING SYSTEMS IN DYNAMIC ENVIRONMENTS

International Journal of Information Acquisition 2010年第3期7卷 225-242页

作者： YI YAO CHUNG-HAO CHEN BESMA ABIDI DAVID PAGE ANDREAS KOSCHAN MONGI ABIDI Visualization and Computer Vision Lab GE Global Research USA Department of Mathematics and Computer Science North Carolina Central University USA Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville USA

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, handoff safety margin is introduced to sensor planning so that sufficient overlapped FOVs among adjacent cameras are reserved for successful and smooth target transition. In this paper, we investigate the sensor planning problem when considering the dynamic interactions between moving targets and observing cameras. The probability of camera overload is explored to model the aforementioned interactions. The introduction of the probability of camera overload also considers the limitation that a given camera can simultaneously monitor or track a fixed number of targets and incorporates the target's dynamics into sensor planning. The resulting camera placement not only achieves the optimal balance between coverage and handoff success rate but also maintains the optimal balance in environments with various target densities. The proposed camera placement method is compared with a reference algorithm by Erdem and Sclaroff. Consistently improved handoff success rate is illustrated via experiments using typical office floor plans with various target densities.

关键词： Sensor planning visual surveillance multiple camera systems camera handoff

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Enhancement for face video from omni-directional video camera

Enhancement for face video from omni-directional video camer...

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Asilomar Conference on Signals, Systems & computers

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research lab ECE Department University of California San Diego La Jolla CA USA

In this paper we propose a novel algorithm to enhance the face video from omni-directional video camera. A two-stage strategy is used. First stage is the noise elimination, realized by iterative MAP update. Naive Bayesian criterion is used to model the posterior. The predominant salt and pepper noise introduced by the omni-to-perspective transformation can be removed effectively, while the image details are well preserved. The high frequency component compensation (HFCC) super-resolution algorithm is applied thereafter to remove the blocky effect. Experimental results show that the video quality has a marked improvement.

关键词： Cameras Iterative algorithms Statistics Robot vision systems Bayesian methods Face detection Image resolution Image enhancement Streaming media Image segmentation

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Real-time tracking of recreational boats in coastal areas using deep learning

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Ocean and Coastal Management 2025年 267.0卷

作者： Signaroli, Marco Lana, Arancha Cutolo, Eugenio Alós, Josep Gonzalez-Cid, Yolanda Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB) Marine Research in Ecological and Social Systems Group (MARES) C/. Miquel Marquès 21 Illes Balears Esporles 07190 Spain AtalaIA Detection Technologies Av. Gabriel Alomar I Villalonga 22 Illes Balears Palma 07006 Spain Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB) DataLab Service C/. Miquel Marquès 21 Illes Balears Esporles 07190 Spain IMT Atlantique Lab-STICC UMR CNRS 6285 and Odyssey Inria/IMT Technopôle Brest-Iroise 655 Avenue du Technopôle Plouzané 29280 France Universitat de les Illes Balears (UIB) Department of Mathematics and Computer Science Systems Robotics and Vision Group (SRV) Cra. de Valldemossa km 7.5 Illes Balears Palma 07122 Spain

To effectively manage and conserve coastal ecosystems, accurate spatial data on marine recreational activities are crucial. This study introduces a deep-learning-based system designed for real-time detection and tracking of recreational vessels in coastal environments using cameras. We fine-tuned and evaluated two object detection and classification algorithms, YOLOv5 and YOLOv7, for automated, real-time vessel detection, classification and positioning within Marine Protected Areas (MPAs). Additionally, we optimized two multiple object tracking algorithms, StrongSORT and ByteTrack, for tracking the movements of the detected vessels in sequential timeframes. We implemented the best combination (YOLOv5 and ByteTrack) on an NVIDIA Jetson platform, an edge computing device specifically designed for AI applications, conducting thorough benchmarking across various simulated hardware configurations to determine its minimal computational and power needs. Then, we conducted field tests by positioning the system on a coastal cliff overlooking a recreational fishery located in a partial MPA. These tests aimed to validate the system's real-time operational viability and to acquire precise vessel trajectories. The results confirmed the system's efficacy and its data collection capabilities within a real marine environment. Finally, we evaluated two camera calibration techniques for converting image trajectories to geographic coordinates: a projective transformation with homography for accurate perspective adjustment, and an innovative neural network-based approach. The system we have developed could markedly enhance the monitoring and surveillance capabilities within MPAs, generating spatial-temporal data of recreational fishing effort that can be easily transferred to other case studies. © 2025 The Authors

关键词： Artificial intelligence Boat detection Deep learning Fishing effort Marine protected areas Multiple object tracking Recreational fishing

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On the complexity of computing equilibria for nonsymmetric analog networks

On the complexity of computing equilibria for nonsymmetric a...

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International Joint Conference on Neural Networks (IJCNN)

作者： D.A. Miller S.W. Sucker Computer Vision and Robotics Laboratory Research Center of Intelligent Machines McGill University Montreal Canada Comput. Vision & Robotics Lab. McGill Univ. Montreal Que. Canada

The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if the amplifiers are piecewise-linear, then such networks are instances of a game-theoretic model known as polymatrix games. Equilibria for the latter may be computed by vertex pivoting algorithms similar to the simplex method for linear programming, which are in practice of low order polynomial complexity. These algorithms appear to be the only ones both guaranteed to work and which are polynomial in practice. These results suggest that networks with few nonstable equilibria would be computationally attractive.< >

关键词： Analog computers computer networks Polynomials Biology computing NP-hard problem computer vision Physics computing Piecewise linear techniques Intelligent robots Robot vision systems

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Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras

arXiv

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arXiv 2023年

作者： Altillawi, Mohammad Pataki, Zador Li, Shile Liu, Ziyuan AI Robotics & Simulation Huawei Munich Research Center Germany Computer Vision Center Universitat Autònoma de Barcelona Spain Computer Vision and Geometry Lab ETH Zurich Switzerland

Re-localizing a camera from a single image in a previously mapped area is vital for many computer vision applications in robotics and augmented/virtual reality. In this work, we address the problem of estimating the 6 DoF camera pose relative to a global frame from a single image. We propose to leverage a novel network of relative spatial and temporal geometric constraints to guide the training of a Deep Network for localization. We employ simultaneously spatial and temporal relative pose constraints that are obtained not only from adjacent camera frames but also from camera frames that are distant in the spatio-temporal space of the scene. We show that our method, through these constraints, is capable of learning to localize when little or very sparse ground-truth 3D coordinates are available. In our experiments, this is less than 1% of available ground-truth data. We evaluate our method on 3 common visual localization datasets and show that it outperforms other direct pose estimation methods. © 2023, CC BY-NC-SA.

关键词： Cameras

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Mobile robot localization using panoramic vision and combinations of feature region detectors

Mobile robot localization using panoramic vision and combina...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Arnau Ramisa Adriana Tapus Ramon Lopez de Mantaras Ricardo Toledo Artificial Intelligence Research Institute Spain Robotics Research Lab Interaction Lab Department of Computer Science University of Southern California Los Angeles USA UAB Campus Computer Vision Center Spain

This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results.

关键词： Mobile robots computer vision Detectors Robot kinematics Robot localization Feature extraction Fingerprint recognition robotics and automation USA Councils Image databases

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Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation

Moving object detection for visual odometry in a dynamic env...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Haram Kim Pyojin Kim H. Jin Kim Lab for Autonomous Robotics Research (LARR) Seoul National University Seoul South Korea Computer Graphics and Vision Lab (GrUVi) Simon Fraser University Burnaby BC

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without depth. For robotic navigation where real-time RGBD data is often readily available, utilization of the depth information would be beneficial for obstacle recognition. Here, we propose a simple moving object detection algorithm that uses RGB-D images. The proposed algorithm does not require estimating a background model. Instead, it uses an occlusion model which enables us to estimate the camera pose on a background confused with moving objects that dominate the scene. The proposed algorithm allows to separate the moving object detection and visual odometry (VO) so that an arbitrary robust VO method can be employed in a dynamic situation with a combination of moving object detection, whereas other VO algorithms for a dynamic environment are inseparable. In this paper, we use dense visual odometry (DVO) as a VO method with a bi-square regression weight. Experimental results show the segmentation accuracy and the performance improvement of DVO in the situations. We validate our algorithm in public datasets and our dataset which also publicly accessible.

关键词： Cameras Heuristic algorithms Object detection Robustness Trajectory Visual odometry Feature extraction

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Robotic Approach Trajectory Using Reinforcement Learning with Dual Quaternions

Robotic Approach Trajectory Using Reinforcement Learning wit...

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Iberian robotics Conference (ROBOT)

作者： Daniel Frau-Alfaro Santiago T. Puente Ignacio De Loyola Páez-Ubieta Edison Velasco-Sánchez AUtomatics RObotics and Artificial Vision Lab University Institute for Computer Research San Vicente Spain University Institute for Engineering Research University Miguel Hernández Elche Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Manipulation tasks in robotics usually involve two phases: an approach to the object and the grasp itself. The first action allows the robot to reach a certain pose in space that is likely to allow the object to be manipulated. Reinforcement Learning (RL) techniques allow a policy to be learned through experience given a set of states and actions, so this is a powerful tool for developing controllers for specific tasks such as positioning the robot in a particular point in space. However, when manipulating an object, orientation is as relevant as position. For this reason, a method of RL for positioning the robot's end effector in a suitable position and orientation for manipulation in simulation is presented. This approach models the problem of computing the distance for the reward function using dual quaternions parameterisation, an element that can represent the pose and attitude of a rigid body in Euclidean space in a compact way without having to apply any constraints.

关键词： Training Quaternions Computational modeling Process control Reinforcement learning Aerospace electronics End effectors Trajectory Robots

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operated Vehicle, Beyond Teleoperation in a New Generation of Underwater Intervention Vehicles

OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operat...

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OCEANS

作者： Roger Pi Joan Esteba Patryk Cieslak Narcís Palomeras Pedro J. Sanz Raúl Marín Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) Universitat de Girona Girona Interactive and Robotic Systems Lab (IRS-Lab) Universitat Jaume I Castelló de la Plana

OPTIHROV is a proof-of-concept project to demonstrate a telerobotic system, based on an untethered, OPTIcally-connected, Hybrid Autonomous-ROV, that aims to replace light intervention ROVs operating at close range to networked infrastructures. The system is designed to bridge the gap between current ROVs, tethered to a Docking Station, and the dream of the future Intervention Autonomous Underwater Vehicle (I-AUV). OPTIHROV is a telerobotic system to perform inspection and light intervention tasks in potentially unknown and unstructured environments. It uses human-robot interaction (HRI) methods that allow the user to remotely guide the robot by launching a set of sensory-motion primitives performed by the robot *** paper presents a proof of concept demonstration of the OPTIHROV concept, where the wireless Hybrid Remotely Operated Vehicle (HROV), located in the test tank of the Universitat de Girona (UdG), is controlled/supervised from the Universitat Jaume I (UJI) in Castelló de la Plana, about 330 km away.

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