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Automated vigilence system for physical clash detection in localized environments 10

Automated vigilence system for physical clash detection in l...

引用

10th international conference on Communication and signal processing, ICCSP 2024

作者： Jennifer, D. Aswini, P. Sundararajan, Kanthimathi Keerthana, D. Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai123 India

ISBN: (纸本)9798350353068

Using MobileNetV3, a compact framework for neural networks and computer vision techniques, this study offers a proposal on how to automate vigilance systems for physical clash detection for continuous monitoring in real time. The primary goal of this system's design was to identify probable physical confrontations between nearby individuals and automatically make an audio message to the appropriate authorities to report a case like this. The system is trained using a real-time violence. Situations video dataset from the MobileNetV3 big model. Through a variety of video inputs, the model's ability to recognize physical collisions is tested, and it demonstrated a precision of 96%. Real-time use of the model's saved weights is made possible by a live video feed, and a phone call alarm is automatically sent to the registered authority using the Twilio API so that immediate action can be taken to prevent more physical encounters. Camera surveillance can be used to deploy the system. As a result, the suggested method represents an important advancement in the field of automated monitoring and can be seen as making a substantial contribution to enhancing security. Overall, this paper highlights the effectiveness of keeping tabs on locations for physical clash detection and the possibility of automated surveillance devices. © 2024 IEEE.

关键词： Computer vision

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vision TRANSFORMER EQUIPPED WITH NEURAL RESIZER ON FACIAL EXPRESSION RECOGNITION TASK 47

VISION TRANSFORMER EQUIPPED WITH NEURAL RESIZER ON FACIAL EX...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hwang, Hyeonbin Kim, Soyeon Park, Wei-Jin Seo, Jiho Ko, Kyungtae Yeo, Hyeon Korea Adv Inst Sci & Technol Daejeon South Korea ACRYL Busan South Korea

ISBN: (纸本)9781665405409

When it comes to wild conditions, Facial Expression Recognition is often challenged with low-quality data and imbalanced, ambiguous labels. This field has much benefited from CNN based approaches;however, CNN models have structural limitations to see the facial regions in distance. As a remedy, Transformer has been introduced to vision fields with a global receptive field but requires adjusting input spatial size to the pretrained models to enjoy its strong inductive bias at hands. We herein raise the question of whether using the deterministic interpolation method is enough to feed low-resolution data to Transformer. In this work, we propose a novel training framework, Neural Resizer, to support Transformer by compensating information and downscaling in a data-driven manner trained with loss function balancing the noisiness and imbalance. Experiments show our Neural Resizer with F-PDLS loss function improves the performance with Transformer variants in general and nearly achieves the state-of-the-art performance.

关键词： Facial Expression Recognition vision Transformer Learnable Resizer Deep Learning

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A robotic Ultrasound System Controlled by Human sEMG signals 7

A Robotic Ultrasound System Controlled by Human sEMG Signals

引用

7th IEEE international conference on Advanced robotics and Mechatronics

作者： Xue, Boyang Xue, Wei Huang, Qinghua Northwestern Polytech Univ Sch Mech Engn Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning OPTMAL Xian 710072 Shaanxi Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

With the rapid development of bioelectric signal processing and corresponding equipment, the processing and recognition of electromyography (EMG) signals are more and more widely used in the field of robotics, helping to achieve seamless communication and interaction between humans and machines. Taking the normative movement of human arms as the research object, this paper studies the classification and recognition method of multichannel surface EMG signals and the control problems of robots during exercise. Based on this, a human-computer interaction control system between the human EMG signal and the robot is designed and realized.

关键词： Ultrasonic imaging Trajectory planning robot control signal processing algorithms Process control signal processing Electromyography

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Exoskeleton Motion Control and Image processing 6

Exoskeleton Motion Control and Image Processing

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Yadav, Gaurav Military College of Telecommunication Engineering Indore Madhya Pradesh Mhow India

ISBN: (纸本)9798350304480

The paper describes the analysis, implementation, and control of a 17 degree of freedom (DOF) robotic exoskeleton model using Accelerometer and Gyroscope (GY-MPU), Electromyography (EMG) and flex sensors. The GY-MPU axis modulation signals are used to interpret human limb movements (arms, shoulder, and legs), while the flex signals detect elbow joints angular displacement measurements. The paper also implements EMG based control on a 5 DOF 3D bionic hand, interpreting hand grasp/release movements via the EMG signal. Skin surface EMG signals of forearm muscles and hand force/forearm torque are used as input information to the proposed controller. Fuzzy control is applied to realize natural and flexible motion assist, and an experiment is performed to evaluate the exoskeleton robot. While by applying the skin surface EMG signals along GY-MPU as the main input signals to the controller, the robot can be controlled automatically without requiring any additional equipment, the image processing capability is also intended to be integrated as part of the sys. An experimental setup includes sensors, control unit, signal-processing board, and actuators. The kinematic analysis of the different DOF of humanoid robot includes forward and inverse kinematics. The GY-MPU are reliable and easy to use, while the EMG and flex sensors require careful signal processing design due to unstable raw signals. The work has potential applications in development of exoskeleton body suite which can enable our soldiers to perform extraordinary actions during all kinds of military war craft such as conventional as well as close quarter combat (CQB) missions with edge cutting AI integrated image processing tools. © 2023 IEEE.

关键词： AI CQB DOF EMG exoskeleton flex sensors GY-MPU

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Datta, Gourav Liu, Zeyu Li, Anni Beerel, Peter A. Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9798350368741

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10. © 2025 IEEE.

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Research on vision control methods based on multi-sensor fusion 6

Research on vision control methods based on multi-sensor fus...

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2023 6th international conference on Power Electronics and Control Engineering, ICPECE 2023

作者： Wang, Yi Qu, Fengrui Wang, Xijun Zeng, Songtao Sun, Qizhen Li, Mengyang Ge, Jiafei Guangzhou Huangpu Power Supply Bureau Guangdong Power Grid Co. Ltd Guangdong Guangzhou China

This paper focuses on the application scenarios of electric power live line working robots, addressing tasks such as live wire stripping and live wire connection/disconnection. It utilizes Mixed Reality (MR) technology for rapid environment modeling and visual signal acquisition. Through the research on robot contact force sensing, real-time force feedback, and obstacle avoidance through the fusion of robot motion paths and environmental data, it establishes a novel electric power live line working robot system centered around dexterous dual arms at the operation end, force feedback teleoperation controllers, multi-sensor fusion acquisition systems, and robot motion planning and control technology. Through prototyping, a demonstration application is realized, showcasing a semi-autonomous electric power live line working robot system that employs remote operation combined with sensor-based MR for task execution. This teleoperation and Mixed Reality-based semi-autonomous electrified operation approach is poised to play a significant role in advancing and demonstrating the use of robots in the field of electrical grid operations. © Published under licence by IOP Publishing Ltd.

关键词： Remote control

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CD-FSOD: A Benchmark For Cross-Domain Few-Shot Object Detection 48

CD-FSOD: A Benchmark For Cross-Domain Few-Shot Object Detect...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xiong, Wuti University of Oulu Center for Machine Vision and Signal Analysis Finland

ISBN: (纸本)9781728163277

In this paper, we propose a study of the cross-domain few-shot object detection (CD-FSOD) benchmark, consisting of image data from a diverse data domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches, including meta-learning FSOD approaches and fine-tuning FSOD approaches. The results show that these methods tend to fall, and even underperform the naive fine-tuning model. We analyze the reasons for their failure and introduce a strong baseline that uses a mutually-beneficial manner to alleviate the overfitting problem. Our approach is remarkably superior to existing approaches by significant margins (2.0% on average) on the proposed benchmark. Our code is available at https://***/FSOD/CD-FSOD. © 2023 IEEE.

关键词： Object detection

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Instance-Aware Hierarchical Structured Policy for Prompt Learning in vision-Language Models 48

Instance-Aware Hierarchical Structured Policy for Prompt Lea...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wu, Xun Wang, Guolong Liu, Zhaoyuan Dang, Xuan Qin, Zheng Tsinghua University School of Software BNRist China University of International Business and Economics China China

ISBN: (纸本)9781728163277

In recent years, learnable prompts have emerged as a major prompt learning paradigm, enhancing the performance of large-scale vision-language pre-trained models in few-shot image classification. However, enhancing methods are often time-consuming and inflexible because 1) class-specific prompts are inefficient in certain situations;2) instance-specific prompts are put in a fixed position. To address these issues, inspired by the coarse-to-fine decision-making paradigm of human, we propose an Instance-Aware Hierarchical-Structured Policy (IAHSP) that integrates instance-specific prompt selection and appropriate position selection using a reinforcement learning fashion. Specifically, IAHSP consists of two sub-policies: 1) the root policy selects the most suitable prompt from the prompts pool, and 2) the leaf policy identifies the optimal position for inserting the selected prompt. We train these two policies iteratively with rewards constraining the prompts while maintaining their diversity. Extensive experiments on 11 public benchmarks demonstrate that our IAHSP significantly boosts the few-shot image classification performance of vision-language pre-trained models, while also exhibiting superior generalization performance. © 2023 IEEE.

关键词： Few-shot Image Classification Prompt Learning Reinforcement Learning vision-Language Pre-trained Models

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GATED MULTIMODAL FUSION WITH CONTRASTIVE LEARNING FOR TURN-TAKING PREDICTION IN HUMAN-robot DIALOGUE 47

GATED MULTIMODAL FUSION WITH CONTRASTIVE LEARNING FOR TURN-T...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Yang, Jiudong Wang, Peiying Zhu, Yi Feng, Mingchao Chen, Meng He, Xiaodong JD AI Beijing Peoples R China Univ Cambridge LTL Cambridge England

ISBN: (纸本)9781665405409

Turn-taking, aiming to decide when the next speaker can start talking, is an essential component in building human-robot spoken dialogue systems. Previous studies indicate that multimodal cues can facilitate this challenging task. However, due to the paucity of public multimodal datasets, current methods are mostly limited to either utilizing unimodal features or simplistic multimodal ensemble models. Besides, the inherent class imbalance in real scenario, e.g. sentence ending with short pause will be mostly regarded as the end of turn, also poses great challenge to the turn-taking decision. In this paper, we first collect a large-scale annotated corpus for turn-taking with over 5,000 real human-robot dialogues in speech and text modalities. Then, a novel gated multimodal fusion mechanism is devised to utilize various information seamlessly for turn-taking prediction. More importantly, to tackle the data imbalance issue, we design a simple yet effective data augmentation method to construct negative instances without supervision and apply contrastive learning to obtain better feature representations. Extensive experiments are conducted and the results demonstrate the superiority and competitiveness of our model over several state-of-the-art baselines.

关键词： Multimodal Fusion Turn-taking Barge-in Endpointing Spoken Dialogue System

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SSDViT: Exploring Siamese and Self Distillation in ViTs for Generalizable Person Re-identification

SSDViT: Exploring Siamese and Self Distillation in ViTs for ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jia, Jieru Yang, Jianchao Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798350368741

Person re-identification (re-ID) models often fail to generalize well when deployed to unseen camera networks with domain shift. Domain generalization (DG) aims to address this dilemma by training a model on source domains that learns domain-invariant, and hence generalizable representations. Most methods in the literature are based on Convolutional Neural Networks (CNNs), while the DG performance of vision Transformers (ViTs) remains relatively unexplored. In contrast to CNNs, ViTs lack explicit inductive biases, which makes it extremely data-hungry and easily overfit to source domains. In this paper, we investigate the generalization ability of ViTs and propose a novel Siamese and Self Distillation vision Transformer (SSDViT) framework towards addressing the DG re-ID problem. To be specific, Siamese Distillation exploits the weak-to-strong consistency regularization at the image level to enforce the strongly perturbed image to yield consistent prediction with its weakly perturbed version, which provides a simple yet effective solution to introduce invariance bias. On the other hand, Self Distillation seeks to impose consistency constraints at the feature level by leveraging intermediate knowledge to improve the robustness of learned representations. The proposed unified framework pursues the equivalence of predictions at both the image and embedding levels, which underpins the generalization capabilities of learned representations and alleviates the risk of overfitting to source domains. Without bells and whistles, the proposed approach achieves a new state-of-the-art on various DG re-ID benchmarks. Codes are available at https://***/yJCTrans/SSDViT. © 2025 IEEE.

关键词： Domain Generalization Knowledge Distillation Person Re-identification vision Transformer

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