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检索条件"任意字段=Proceedings of the Workshop on Physics-Based Modeling in Computer Vision"

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MMSports 2023 - proceedings of the 6th International workshop on Multimedia Content Analysis in Sports, Co-located with: MM 2023

MMSports 2023 - Proceedings of the 6th International Worksho...

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6th ACM International workshop on Multimedia Content Analysis in Sports, MMSports 2023, co-located with ACM Multimedia 2023

ISBN: (纸本)9798400702693

The proceedings contain 20 papers. The topics discussed include: SkiTech: an alpine skiing and snowboarding dataset of 3D body pose, sole pressure, and electromyography;personalized speech-based heart rate categorization using weighted-instance learning;generating factually consistent sport highlights narrations;DeepSportradar-v2: a multi-sport computer vision dataset for sport understandings;video-based skill assessment for golf: estimating golf handicap;automatic edge error judgment in figure skating using 3D pose estimation from a monocular camera and IMUs;context-aware 3D object localization from single calibrated images: a study of basketballs;event-based high-speed ball detection in sports video;mitigating motion blur for robust 3D baseball player pose modeling for pitch analysis;and expected goals prediction in professional handball using synchronized event and positional data.

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CoLInS 2023 - proceedings of the 7th International Conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems workshop

CoLInS 2023 - Proceedings of the 7th International Conferenc...

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7th International Conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems workshop, CoLInS 2023

The proceedings contain 47 papers. The topics discussed include: the framework of estimation of the impact of the Russian war on the infectious diseases spreading;ontological approach in the smart data paradigm as a basis for open data semantic markup;mathematical modeling to evaluate the accuracy of computer vision for the near-zero motion detection of astronomical objects;improving accuracy by ensuring invariance of two-dimensional binary images in intelligent systems;face detection for video surveillance-based security system;sentinel-2 and Modis data fusion for generation of daily cloud-free images at the sentinel resolution level;improving speaker verification model for low-resources languages;and knowledge base of intelligent information system for prediction of phase stability of solid solutions.

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PhysID: physics-based Interactive Dynamics from a Single-view Image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gothe, Sourabh Vasant Chattopadhyay, Ayon Kiran, Gunturi Venkata Sai Phani Pratik Agarwal, Vibhav Vachhani, Jayesh Rajkumar Ghosh, Sourav Parameswaranath, V.M. Barath Raj, K.R. Samsung R&D Institute India - Bangalore India

ISBN: (纸本)9798350368741

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness. © 2025 IEEE.

关键词： 3D Modelling Diffusion Multimodal Large Language Models physics-based Rendering

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SynthNet: Leveraging Synthetic Data for 3D Trajectory Estimation from Monocular Video 7

SynthNet: Leveraging Synthetic Data for 3D Trajectory Estima...

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7th ACM International workshop on Multimedia Content Analysis in Sports, MMSports 2024

作者： Ertner, Morten Holck Konglevoll, Sofus Schou Ibh, Magnus Graßhof, Stella IT-University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400711985

Reconstructing 3D trajectories from video is often cumbersome and expensive, relying on complex or multi-camera setups. This paper proposes SynthNet, an end-to-end pipeline for monocular reconstruction of 3D tennis ball trajectories. The pipeline consists of two parts: Hit and bounce detection and 3D trajectory reconstruction. The hit and bounce detection is performed by a GRU-based model, which segments the videos into individual shots. Next, a fully connected neural network reconstructs the 3D trajectory through a novel physics-based training approach relying on purely synthetic training data. Instability in the training loop caused by relying on Euler-time integration and camera projections is circumvented by our synthetic approach, which directly calculates loss from estimated initial conditions, improving stability and performance. In experiments, SynthNet is compared to an existing reconstruction baseline on a number of conventional and customized metrics defined to validate our synthetic approach. SynthNet outperforms the baseline based on our own proposed metrics and in a qualitative inspection of the reconstructed 3D trajectories. © 2024 Copyright held by the owner/author(s).

关键词： 3D Reconstruction Ball Tracking computer vision Differential Equations Machine Learning in Sports Neural Network Synthetic Data

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BiLSCCS: Modular dynamical on-road objects trajectory prediction approach 10

BiLSCCS: Modular dynamical on-road objects trajectory predic...

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10th IEEE International workshop on Metrology for AeroSpace, MetroAeroSpace 2023

作者： Saetchnikov, Ivan Skakun, Victor Tcherniavskaia, Elina Belarusian State University Radiophysics Department Minsk Belarus Belarusian State University Physics Department Minsk Belarus

ISBN: (纸本)9781665456906

Dynamical object trajectory prediction is one of the most sophisticated tasks in computer vision, but is a highly urgent problem applied to robotics and autonomous vehicles. In this paper the novel dynamical object trajectory prediction framework biLSCCS is presented based on a six-step approach: object detector based on YOLOv5, bidirectional LSTM encoder, mean-shift multimodal clustering, 4-layer MLP-based classification, synthesis, and bidirectional LSTM decoder. The proposed approach has been tested on two benchmark datasets for object tracking and trajectory prediction, namely ETH and Stanford Drone, which involve on-road dynamic objects observed from aerial view. The experimental findings suggest that the biLSCCS approach demonstrates competitive accuracy and robustness performance in comparison to the SGAN and STAR methods. Specifically, the approach achieves ADE, FDE, and IoU scores of 0.32, 0.72, and 0.55, respectively, on the ETH dataset, and 0.22, 0.51, and 0.49, respectively, on the Stanford Drone dataset. © 2023 IEEE.

关键词： Drones

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ViFiT: Reconstructing vision Trajectories from IMU and Wi-Fi Fine Time Measurements 3

ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi...

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3rd ACM MobiCom workshop on Integrated Sensing and Communication Systems, ISACom 2023

作者： Cao, Bryan Bo Alali, Abrar Liu, Hansi Meegan, Nicholas Gruteser, Marco Dana, Kristin Ashok, Ashwin Jain, Shubham Stony Brook University United States Old Dominion University United States Saudi Electronic University Saudi Arabia Rutgers University United States Georgia State University United States

ISBN: (纸本)9798400703645

Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on. In computer vision domain, tracking is usually achieved by first detecting subjects, then associating detected bounding boxes across video frames. Typically, frames are transmitted to a remote site for processing, incurring high latency and network costs. To address this, we propose ViFiT, a transformer-based model that reconstructs vision bounding box trajectories from phone data (IMU and Fine Time Measurements). It leverages a transformer's ability of better modeling long-term time series data. ViFiT is evaluated on Vi-Fi Dataset, a large-scale multimodal dataset in 5 diverse real world scenes, including indoor and outdoor environments. Results demonstrate that ViFiT outperforms the state-of-the-art approach for cross-modal reconstruction in LSTM Encoder-Decoder architecture X-Translator and achieves a high frame reduction rate as 97.76% with IMU and Wi-Fi data. © 2023 ACM.

关键词： Object detection

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Camera Settings as Tokens: modeling Photography on Latent Diffusion Models 24

Camera Settings as Tokens: Modeling Photography on Latent Di...

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2024 SIGGRAPH Asia Conference-SIGGRAPH Asia

作者： Fang, I-Sheng Han, Yue-Hua Chen, Jun-Cheng Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9798400711312

Text-to-image models have revolutionized content creation, enabling users to generate images from natural language prompts. While recent advancements in conditioning these models offer more control over the generated results, photography-a significant artistic domain-remains inadequately integrated into these systems. Our research identifies critical gaps in modeling camera settings and photographic terms within text-to-image synthesis. vision-language models (VLMs) like CLIP and OpenCLIP, which typically drive the text conditions through cross-attention mechanisms of conditional diffusion models, struggle to represent numerical data like camera settings effectively in their textual space. To address these challenges, we present CameraSettings20k, a new dataset aggregated from RAISE [Dang-Nguyen et al. 2015], DDPD [Abuolaim and Brown 2020], and PPR10K [Liang et al. 2021]. Our curated dataset offers normalized camera settings for over 20,000 raw-format images, providing equivalent values standardized to a full-frame sensor. Furthermore, we introduce Camera Settings as Tokens, an embedding approach leveraging the LoRA adapter of Latent Diffusion Models (LDMs) to numerically control image generation based on photographic principles like focal length, aperture, film speed, and exposure time. Our experimental results demonstrate the effectiveness of the proposed approach to generate promising synthesized images obeying the photographic principles given the specified numerical camera settings. Furthermore, our work not only bridges the gap between camera settings and user-friendly photographic control in image synthesis but also sets the stage for future explorations into more physics-aware generative models.

关键词： Image & Video Generative AI Computational Photography Neural Rendering Perception Image-based modeling Diffusion models

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A High Accuracy, Light Changing Insensitive, Anti-Interference Workpiece Localization Method based on Light Weight Neural Networks 13

A High Accuracy, Light Changing Insensitive, Anti-Interferen...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Xu, Wenfang Liu, Yichen Li, Lunxing Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China Institute of Modern Physics Chinese Academy of Sciences China Shenyang CASNC Technology Co. Ltd. Shenyang110168 China

ISBN: (纸本)9798350315196

A CRTS-III ballastless track plate has been widely used in the railway infrastructure. The anchor sealing using automated industrial robots with computer vision is an essential step of track plates production. This paper proposes a novel visual localization algorithm based on one-dimensional convolutional neural network models with high-precision and strong anti-interference ability used for anchor sealing robots. Our method not only has strong robustness to various noise interference during image generation and transmission and oil pollution on the surface of the workpiece, but has strong robustness to changes of workshop's light without pre-set thresholds or any adjustive parameters as well. The adaptability to over bright or over dark production environments can effectively improve the adaptability of the industrial robot vision system to the production environment. In addition, the neural network model used in our method is small enough, which does not require high equipment computing power, especially GPU, and can run at high speed on a pure CPU industrial control computer platform. © 2023 IEEE.

关键词： Neural network models

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Enhanced vision-based Human Fall Detection with Mask-RCNN and Autoencoder-LSTM Hybrid Framework 6

Enhanced Vision-Based Human Fall Detection with Mask-RCNN an...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Chakraborty, Sritama Ghosh, Mili Department of Computer Science & Technology University of North Bengal India

Elderly individuals who reside independently face a heightened risk of experiencing serious harm due to accidental falls, a leading contributor to mortality rates in this demographic. Fall detection is a critical part of health care for older adults. This paper introduces a methodology for identifying falls among elderly individuals utilizing machine learning algorithms. We suggest utilizing Mask R-CNN, Autoencoder- LSTM, and a Hybrid Mask R-CNN-Autoencoder-LSTM framework to detect falls in a video surveillance environment. Primarily, these algorithms are trained to identify deformations in human body shapes and postures, enabling them to detect whether a fall has occurred. At their core, these algorithms are instructed to distinguish shifts in human body structures and movements, empowering them to flag potential fall occurrences. Our findings suggest that the Mask R-CNN and Autoencoder- LSTM models perform well independently, with the Mask R-CNN excelling in spatial feature extraction and the Autoencoder-LSTM effectively modeling the temporal changes in body movements. However, the hybrid Mask R-CNN-Autoencoder-LSTM approach does not fully capitalize on the strengths of both models, leading to a slightly lower performance than the individual models. Despite this, both Mask R-CNN and Autoencoder-LSTM present a reliable solution for fall detection, with the hybrid framework offering potential improvements for future research. © 2025 Copyright for this paper by its authors.

关键词： Fall detection

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Aggregate Parametric Representation of Image Structural Description in Statistical Classification Methods 5

Aggregate Parametric Representation of Image Structural Desc...

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5th International workshop on computer modeling and Intelligent Systems, CMIS 2022

作者： Gadetska, S. Gorokhovatskyi, V. Stiahlyk, N. Vlasenko, N. Kharkiv National Automobile and Road University Kharkiv Ukraine Kharkiv National University of Radio Electronics Kharkiv Ukraine Educational and Scientific Institute Karazin Banking Institute V.N. Karazin Kharkiv National University Kharkiv Ukraine Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine

Finding effective classification solutions based on the study of the processed data nature is one of the important tasks in modern computer vision. Statistical distributions are a perfect tool for presenting and analyzing visual data in image recognition systems. They are especially effective when creating new feature spaces, particularly, by aggregating descriptor sets in some appropriate way, including bits. For this purpose, it is natural to apply the number of criteria designed to compare the distribution parameters of the analyzed samples. The article develops a speed-efficient method of image classification by introducing aggregate statistical features for the composition of the description components. The metric classifier is based on the use of statistical criteria to assess the significance of the classification decision. The developed classification method based on the aggregation of the feature image set is implemented;the workability of the proposed classifier is confirmed. On the examples of the application of variants of the method for the system of the real images features, its effectiveness was experimentally evaluated. © 2022 Copyright for this paper by its authors.

关键词： computer vision

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