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IEEE International Conference on Image Processing

作者： Kuk-jin Yoon Yoojin Choi In So Kweon Department Electrical Engineering and Computer Science KAIST Robotics and Computer Vision Laboratory South Korea Dept. Electrical Engineering and Computer Science Robotics and Computer Vision Lab. Korea

In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.

关键词： Reflection Image representation Layout Color Polarization computer vision Image segmentation Filters Lighting Argon

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Nonlinear Model Predictive Formation Control for Quadcopters

Nonlinear Model Predictive Formation Control for Quadcopters

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作者： Ribeiro, Tiago T. Conceição, André G.S. Sa, Inkyu Corke, Peter LaR - Robotics Laboratory Department of Electrical Engineering Federal University of Bahia Salvador Brazil Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Australia

This paper presents a Nonlinear Model Predictive Formation Control for a group of Quadcopters. In this approach the controllers are distributed to the quadcopters and the coupling is done through its objective function. The path following problem is expressed through a dynamic model of the quadcopter's state errors relative to a Serret-Frenet frame, and the coordination problem is solved through adjustments in the robots' speeds combined with the path update rate for each robot. A strategy based on constraints relaxing is proposed to solve the problem of high state and formation errors, factors that directly affect feasibility and stability. Simulation results considering a realistic scenario are provided to demonstrate the performance of the proposed control strategy. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Improving stereo camera depth measurements and benefiting from intermediate results

Improving stereo camera depth measurements and benefiting fr...

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IEEE Symposium on Intelligent Vehicle

作者： C. Høilund T. B. Moeslund C. B. Madsen M. M. Trivedi Laboratory of Computer Vision and Media Technology Aalborg University Denmark Computer Vision and Robotics Research Laboratory University of California San Diego USA

This paper presents a method for improving the disparity values obtained with a stereo camera by applying an iconic Kalman filter and known ego-motion. The improvements are demonstrated in an application of determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing variance and increasing the density of the filtered disparity map. Then, a stochastic occupancy grid is calculated from the filtered disparity map, providing a top-down view of the scene where the uncertainty of disparity measurements are taken into account. These occupancy grids are segmented to indicate a maximum depth free of obstacles, enabling the marking of free space in the accompanying intensity image. Even without motion of the camera, the quality of the disparity map is increased significantly. Applications of the intermediate results are discussed, enabling features such as motion detection and quantifying the certainty of the measurements. The evaluation shows significant improvement in disparity variance and disparity map density, and consequently an improvement in the application of marking free space.

关键词： Filters Stereo vision Intelligent vehicles Robot vision systems Layout USA Councils Smart cameras Laboratories computer vision Motion detection

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Development of Hard Hat Wearing Monitoring System Using Deep Neural Networks with High Inference Speed

Development of Hard Hat Wearing Monitoring System Using Deep...

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2020 International Russian Automation Conference, RusAutoCon 2020

作者： Filatov, Nikolay Maltseva, Natalia Bakhshiev, Aleksandr Russian State Scientific Center for Robotics and Technical Cybernetics Laboratory of Computer Vision Systems Saint-Petersburg Russia Peter the Great St. Petersburg Polytechnic University Saint-Petersburg Russia

ISBN: (纸本)9781728161303

Personal protection equipment is essential for safe working conditions on a construction sites and other industrial enterprises. However, there is a number of employee who violate safety precautions, thus, taking additional risks. To avoid accidents strict supervision should be applied. In this work an automatic hard hat wearing detection system from surveillance cameras is developed. To save computational resources small neural networks with high inference speed are applied. For the training of neural networks, a dataset consisting of several industrial scenes and 1478 images was composed. Frame based precision of SqueezeDet neural network for object detection was increased by 9% with the use of additional MobileNets classifier without significant decrease in operating speed. The overall system achieved 0.75 F1-score on a test dataset. Therefore, the developed hard hat wearing monitoring systems is suitable for enhancing on-site safety of workers. © 2020 IEEE.

关键词： Object detection

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Throat microphone signal for speaker recognition 8

Throat microphone signal for speaker recognition

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8th International Conference on Spoken Language Processing, ICSLP 2004

作者： Shahina, A. Yegnanarayana, B. Kesheorey, M.R. Speech and Vision Laboratory Department of Computer Science and Engg. Indian Institute of Technology Madras Chennai600 036 India Center for Artificial Intelligence and Robotics Bangalore560 001 India

Speaker recognition systems perform better when clean speech signals are used for the task. In the presence of high levels of background noise, speech recorded from a close speaking microphone will be degraded and hence the performance of the speaker recognition system. Use of a transducer held at the throat results in a signal that is clean even in a noisy environment. This paper discusses the prospect of using such signals for speaker recognition. A study of a text-independent speaker recognition system based on features extracted from speech simultaneously recorded using a throat microphone and a close-speaking microphone in clean and simulated noisy conditions is conducted. Autoassociative neural networks are used to model the speaker characteristics based on the vocal tract system and excitation source features represented by weighted linear prediction cepstral coefficients and linear prediction residual, respectively. The results of experimental studies show that the speech collected from the throat microphone can be used for tasks like speaker recognition, especially in noisy conditions.

关键词： Microphones

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Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions

Human body modelling and tracking using volumetric represent...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Cuong Tran Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Articulated human body modeling and tracking from vision data is an attractive research area with many potential applications. There has been a tremendous amount of related research works in this area. Therefore, having a comprehensive insight into high quality existing works and awareness of the research frontier in the area is essential for follow-up research studies. With that objective, this paper provides a review of the subarea of model based methods for human body modeling and tracking using volumetric (voxel) data. We will focus on analyzing and comparing some recent techniques, especially which are in the past two years, in order to highlight trends in the domain as well as to point out limitations of the current state of the art. Based on this analysis, we will discuss our idea of combining Laplacian Eigenspace (LE) based voxel segmentation [20] and Kinematically Constrained Gaussian mixture model (KC-GMM) method [3] to have a more powerful human body pose estimation system as well as discuss other possibilities for future work.

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Learning multi-lane trajectories using vehicle-based vision

Learning multi-lane trajectories using vehicle-based vision

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Sayanan Sivaraman Brendan Morris Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, this work proposes a framework for understanding the driving situation based on vehicle mounted vision sensors. Vehicles are tracked using Kalman filtering based on a vision-based system that detects and tracks using a combination of monocular and stereo-vision. The vehicles' full trajectories are recorded, and a data-driven learning framework has been applied to automatically learn surround behaviors. By learning based on observations, the ADAS system is being trained by experience. Learned trajectories have been compared between dense and free-flowing traffic conditions. Preliminary experimental results using real-world multi-lane highways show the basic promise of this approach. Future research directions are discussed.

关键词： Vehicles Trajectory Hidden Markov models Mathematical model Detectors Equations Training

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Semantic Visual Simultaneous Localization and Mapping: A Survey

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7426-7449页

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Zhejiang University of Technology Institute of Computer Vision College of Computer Science and Technology Hangzhou 310023 China Istituto Italiano di Tecnologia Human-Robot Interfaces and Interaction Laboratory Genoa 16163 Italy National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Universitã di Genova Ph.D. Program of National Interest in Robotics and Intelligent Machines (DRIM) Genoa 16126 Italy Hangzhou Normal University School of Information Science and Technology Hangzhou 311121 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby 2800 Denmark Tianjin University of Technology Institute of Computer Vision School of Computer Science and Engineering Tianjin 300384 China

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emphasizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications. © 2000-2011 IEEE.

关键词： localization mapping semantic information Semantic visual SLAM SLAM datasets

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Factorization-based segmentation of motions

Factorization-based segmentation of motions

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Workshop on Visual Motion

作者： T.E. Boult L. Gottesfeld Brown Vision and Robotics Laboratory Department of Computer Science Columbia University NY USA

The authors address the problem of motion segmentation using the singular value decomposition of a feature track matrix. It is shown that, under general assumptions, the number of numerically nonzero singular values can be used to determine the number of motions. Furthermore, motions can be separated using the right singular vectors associated with the nonzero singular values. A relationship is derived between a good segmentation, the number of nonzero singular values in the input and the sum of the number of nonzero singular values in the segments. The approach is demonstrated on real and synthetic examples. The paper ends with a critical analysis of the approach.< >

关键词： Matrix decomposition Image segmentation Tracking Singular value decomposition Layout Shape Eigenvalues and eigenfunctions Robot vision systems Laboratories computer science

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Motion based vehicle surround analysis using an omni-directional camera

Motion based vehicle surround analysis using an omni-directi...

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IEEE Symposium on Intelligent Vehicle

作者： T. Gandhi M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Omni-directional cameras which give 360 degree panoramic view of the surroundings and have recently been used in many applications such as robotics, navigation and surveillance. This paper describes the application of motion estimation on omni camera to perform surround analysis using an automobile mounted camera. The system detects and tracks the surrounding vehicles by compensating the ego-motion and detecting objects having independent motion. Prior knowledge about ego-motion and calibration is optimally combined with the information from the image gradients to get better motion compensation.

关键词： Motion analysis Cameras Robot vision systems Object detection Motion detection Vehicle detection Navigation Surveillance Motion estimation Image motion analysis

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