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image-Based visual Servoing Switchable Leader-follower Control of Heterogeneous Multi-agent Underwater Robot System

Image-Based Visual Servoing Switchable Leader-follower Contr...

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Kanzhong Yao Nathalie Bauschmann Thies L Alff Wei Cheah Daniel A Duecker Keir Groves Ognjen Marjanovic Simon Watson Department of Electrical and Electronic Engineering Manchester Centre for Robotics and AI University of Manchester UK Institute of Mechanics and Ocean Engineering Hamburg University of Technology Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany

Confined and cluttered aquatic environments present a number of significant challenges with respect to inspection by robotic platforms, including localisation and communications. Some of these can be mitigated by using collaborative heterogeneous multi-robot teams. An important element of such a system is collaborative control. This paper addresses this challenge by presenting an image-Based visual Servoing (IBVS), leader-follower control system for heterogeneous aquatic robots. Experiments were conducted in an uncluttered pond to demonstrate the capabilities of the system. The results show robots can maintain tracking each other with maximum $x$ and $y$ displacements of 0.42 m and 0.41 m, the maximum projection distance in the xy-plane of maintaining formation is 0.45 m, showing the stability and feasibility of deploying such system on underwater platforms.

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The Problem of Detecting Boxers in the Boxing Ring 14th

The Problem of Detecting Boxers in the Boxing Ring

引用

14th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Stefanski, Piotr Kozak, Jan Jach, Tomasz Univ Econ Katowice Dept Machine Learning 1 Maja PL-40287 Katowice Poland

ISBN: (纸本)9789811982330;9789811982347

Modern technology is strongly associated with sports. A perfect example of machine learning in sports is a support of detection of specific events or situations. Such a problem is present in boxing, where boxers' moves need to be precisely detected. However video analysis is labor intensive but may provide valuable information. The paper presents the problem of processing recordings of boxing boxers, in which the dynamics is at an extremely high level and some events last for fractions of seconds. Additionally, the competition is often watched by spectators blocking the view. The goal of this paper is to present accurate, precise and quick method of detecting the presence of pugilists in the ring. This will allow to evaluate and score the boxing fight later. To validate the experiment, relevant material had to be collected - the authors recorded real boxing bouts and prepared the complete training set. The proposed solution will be used to automatically filter-out uninteresting parts of footage, where boxers are not engaged in close-combats situation.

关键词： Boxer detection Video processing image processing Dynamic sport analysis

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Underwater Object Detection Integrated with image Enhancement

Underwater Object Detection Integrated with Image Enhancemen...

引用

ieee International conference on Real-time Computing and Robotics (RCAR)

作者： Junjie Wen Jinqiang Cui Guidong Yang Benyun Zhao Zhi Gao Ben M. Chen Department of Mechanical and Automation Engineering the Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Despite the significant achievements made in deep learning for object detection, the challenging conditions of underwater environments severely affect image quality, thereby adversely impacting the effectiveness of detecting underwater objects. Current techniques often employ image enhancement prior to processing to improve the visual quality of underwater images, yet this method incurs extra computational costs and delays, with a risk of diminishing the overall performance of detection. To address these concerns, an efficient framework is presented for underwater object detection, integrating underwater image enhancement with a multi-phase training approach. The framework entails the utilization of both detection and enhancement heads within a unified network backbone, leveraging separate datasets for training purposes. Furthermore, a multi-phase training methodology is introduced to appropriately balance the joint training of detection and enhancement tasks. Extensive experimentation validates that the network proposed in this study attains state-of-the-art performance when compared with other underwater object detectors.

关键词： Training image quality visualization Head Object detection Detectors Real-time systems

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Prototyping a Low-Cost Pest Detection System for CNT0718-26-1-1-1 Rice Farming 27

Prototyping a Low-Cost Pest Detection System for CNT0718-26-...

引用

27th International Computer Science and Engineering conference, ICSEC 2023

作者： Rangubtook, Ratthapoom Plubplai, Thapanan Jirakajohnkool, Supet Rattanatamrong, Prapaporn Thammasat University Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350342109

Pests are significant and challenging issues to handle for rice farming and have negative influence on rice quality and production. Various fields of applications are now using real-time object detection technology, which transforms visual data into quantitative data that may be later examined in more detail. This technology has also been applied in pest detection systems along with real-time notification. This paper presents the design and implementation of a digital technology tools for rice farmers to handle pest issues by using an ESP32 with an OV2640 camera in order to take pictures of pests and transfer the captured pictures to Google Drive for further processing. We developed a pest detection model using the Google's TensorFlow framework. The model was trained using 50 images for each of four pest types, namely rice roller, rice gall midge, rice earhead and rice stem borer. The trained model is then used for detecting pests in the captured images stored in Google Drive. Real-time notification is sent when the insect pests of interest are detected to users via the LINE application. Since the amount of image data available to train the model is rather small, our initial pest detection model can achieve only 65 percent accuracy. We explored possibility in improving accuracy of the model by conducting a comparison study between using an ensemble of multiple binary-class pest classifiers versus the original single multiple-class pest classifier. From our evaluation results, the use of multiple binary-class classification method can improve the accuracy/precision by up to 80 percent without the need to increase training images, © 2023 ieee.

关键词： image classification

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End-to-End Unsupervised Sketch to image Generation

End-to-End Unsupervised Sketch to Image Generation

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xingming Lv Lei Wu Zhenwei Cheng Xiangxu Meng School of Software ShanDong University Jinan China

A freehand sketch is geometrically distorted and lacks colors, textures, and other visual details. This leads to challenges in freehand sketch-to-image generation tasks. Many existing works take a multistage strategy to generate images from sketches, and they depend on the shape of the input sketch badly and ignore the shape adjustment of the generated image. More importantly, the generated images are of low quality with distorted textures and colors, and shape deformation. In order to overcome the above challenges, we propose an end-to-end method to accomplish the freehand sketch-to-image task, and the proposed architecture is based on an unsupervised network. Our key insight is to propose a shape discriminator to provide shape constraints for the generated image. Besides, we introduce image-Global Attention(IGA) and Focal Frequency Loss(FFL). IGA and FFL mainly focuses on the whole image and every patch of an image, respectively. We also extend a new dataset called NewGiraffe based on the giraffe class of SketchyCOCO, and our approach is validated on two datasets: Shoes and NewGiraffe. Through qualitative and quantitative results, we demonstrate our method’s ability to generate realistic images from freehand sketches.

关键词： visualization Acoustic distortion Shape image color analysis image synthesis Deformation Footwear

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A Lightweight VK-Net Based on Motion Profiles for Hazardous Driving Scenario Identification 23

A Lightweight VK-Net Based on Motion Profiles for Hazardous ...

引用

23rd ieee International conference on High Performance Computing and communications, 7th ieee International conference on Data Science and Systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Zhen Xu, Jingning Zheng, Jiang Yu Fan, Hongfei Yu, Rongjie Zong, Jiaqi Li, Xinyi School of Software Engineering Tongji University Shanghai China Indiana University-Purdue University Department of Computer Science Indianapolis United States Jiangsu China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665494571

Hazardous driving scenarios are critical for the development and testing of highly autonomous vehicles. Recent studies have proposed new approaches for extracting hazardous scenarios from driving videos, which greatly improve the detection accuracy compared with traditional approaches that extract information from structured kinematic data. However, direct processing on driving videos is costly, and the videos need to be uploaded and processed in a centralized manner. In this study, we propose a novel approach for reducing the cost of video analysis while maintaining high accuracy in hazardous scenario identification. Firstly, the videos are significantly compressed into motion profile images that contain motion trajectories of objects. Secondly, a lightweight CNN with attention mechanism has been designed to extract risky trajectory features of the conflicting traffic objects. Furthermore, data augmentation has been applied to increase the generalizability of the model. The proposed approach achieves significantly lightweight network size, and meanwhile, experimental results have demonstrated that the approach achieves high accuracy with sensitivity 0.95, specificity 0.85 and AUC 0.95, which outperforms state-of-the-art hazardous scenario identification approaches. The efficient execution of identification implies that it is suitable to be distributed and deployed at each vehicle, and consequently, videos could be processed via edge computing, which would improve the diversity of hazardous scenarios and the efficiency of scenario collection for further research work. © 2021 ieee.

关键词： Sensitivity image coding Smart cities Kinematics Feature extraction Trajectory Data mining

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S-VVAD: visual Voice Activity Detection by Motion Segmentation

S-VVAD: Visual Voice Activity Detection by Motion Segmentati...

引用

ieee Winter conference on Applications of Computer Vision (WACV)

作者： Shahid, Muhammad Beyan, Cigdem Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Genoa DITEN Genoa Italy Univ Verona Dept Comp Sci Verona Italy Huawei Technol Ltd Ireland Res Ctr Dublin Ireland

ISBN: (纸本)9780738142661

We address the challenging Voice Activity Detection (VAD) problem, which determines "Who is Speaking and When?" in audiovisual recordings. The typical audio-based VAD systems can be ineffective in the presence of ambient noise or noise variations. Moreover, due to technical or privacy reasons, audio might not be always available. In such cases, the use of video modality to perform VAD is desirable. Almost all existing visual VAD methods rely on body part detection, e.g., face, lips, or hands. In contrast, we propose a novel visual VAD method operating directly on the entire video frame, without the explicit need of detecting a person or his/her body parts. Our method, named S-VVAD, learns body motion cues associated with speech activity within a weakly supervised segmentation framework. Therefore, it not only detects the speakers/not-speakers but simultaneously localizes the image positions of them. It is an end-to-end pipeline, person-independent and it does not require any prior knowledge nor pre-processing. S-VVAD performs well in various challenging conditions and demonstrates the state-of-the-art results on multiple datasets. Moreover, the better generalization capability of S-VVAD is confirmed for cross-dataset and person-independent scenarios.

关键词： Voice activity detection visualization Computer vision Privacy image segmentation Motion segmentation Lips

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Memory-Centric Architecture of Neural processing Unit for Edge Device 18

Memory-Centric Architecture of Neural Processing Unit for Ed...

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18th International SoC Design conference (ISOCC)

作者： Lee, Eunchong Sung, Minyong Jang, Sung-Joon Park, Jonghee Lee, Sang-Seol Korea Elect Technol Inst Intelligent Image Proc Res Ctr Geonggi Do South Korea

ISBN: (纸本)9781665401746

The deep learning algorithms require high computational power and memory usage to provide better performance for users. However, the edge devices use different sizes of deep learning networks depending on the application and require hardware optimization due to the limited hardware resources. To minimize the hardware redesign efforts by the networks, we propose a Neural processing Unit (NPU) hardware consisting of one SRAM and 16 processing Element (PEs) that enables various parallel configurations. In this paper, we introduce the NPU hardware details and several combinations of parallel hardware structure. We also demonstrate that our hardware can handle a variety of networks by describing hardware behavior under the data configuration that is written to SRAM

关键词： Neural processing Unit Deep Learning processing Element image processing

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Thqa: A Perceptual Quality Assessment Database for Talking Heads

Thqa: A Perceptual Quality Assessment Database for Talking H...

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ieee International conference on image processing

作者： Yingjie Zhou Zicheng Zhang Wei Sun Xiaohong Liu Xiongkuo Min Zhihua Wang Xiao-Ping Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China PengCheng Laboratory China Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Tsinghua Shenzhen International Graduate School Tsinghua University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology. However, the manual modeling and control required for the majority of digital humans pose significant obstacles to efficient development. The speech-driven methods offer a novel avenue for manipulating the mouth shape and expressions of digital humans. Despite the proliferation of driving methods, the quality of many generated talking head (TH) videos remains a concern, impacting user visual experiences. To tackle this issue, this paper introduces the Talking Head Quality Assessment (THQA) database, featuring 800 TH videos generated through 8 diverse speechdriven methods. Extensive experiments affirm the THQA database’s richness in character and speech features. Subsequent subjective quality assessment experiments analyze correlations between scoring results and speech-driven methods, ages, and genders. In addition, experimental results show that mainstream image and video quality assessment methods have limitations for the THQA database, underscoring the imperative for further research to enhance TH video quality assessment. The THQA database is publicly accessible at https://***/zyj-2000/THQA.

关键词： visualization Databases Shape image processing Mouth Manuals Media

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Bi-Directional Tracklet Embedding for Multi-Object Tracking

Bi-Directional Tracklet Embedding for Multi-Object Tracking

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ieee International conference on image processing

作者： H. Çağrι Bilgi A. Aydιn Alatan Department of Electrical and Electronics Engineering Center for Image Analysis (OGAM) METU Ankara Turkey

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The last decade has seen significant advancements in multi-object tracking, particularly with the emergence of deep learning based methods. However, many prior studies in online tracking have primarily focused on enhancing track management or extracting visual features, often leading to hybrid approaches with limited effectiveness, especially in scenarios with severe occlusions. Conversely, in offline tracking, there has been a lack of emphasis on robust motion cues. In response, this approach aims to present a novel solution for offline tracking by merging tracklets using some recent promising learning-based architectures. We leverage a jointly performing Transformer and Graph Neural Network (GNN) encoder to integrate both the individual motions of targets and their interactions in between. By enabling bi-directional information propagation between the Transformer and the GNN, proposed model allows motion modeling to depend on interactions, and conversely, interaction modeling to depend on the motion of each target. The proposed solution is an end-to-end trainable model that eliminates the requirement for any handcrafted short-term or long-term matching processes. This approach performs on par with state-of-the-art multi-object tracking algorithms, demonstrating its effectiveness and robustness.

关键词： Deep learning visualization Target tracking Merging Bidirectional control Transformers Feature extraction

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