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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Evaluating the Immediate Applicability of Pose Estimation for Sign Language recognition

Evaluating the Immediate Applicability of Pose Estimation fo...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Moryossef, Amit Tsochantaridis, Ioannis Dinn, Joe Camgoez, Necati Cihan Bowden, Richard Jiang, Tao Rios, Annette Muller, Mathias Ebling, Sarah Bar Ilan Univ Ramat Gan Israel Google Mountain View CA 94043 USA Univ Surrey Guildford Surrey England Univ Zurich Zurich Switzerland

ISBN: (纸本)9781665448994

Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.

关键词： Analytical models Visualization computer vision Assistive technology Face recognition conferences Pose estimation

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Efficient Pest Classification Using Lightweight Neural Networks for Sustainable Pest Control 4

Efficient Pest Classification Using Lightweight Neural Netwo...

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4th International conference on computer Communication and Artificial Intelligence (CCAI)

作者： Yang, Jiaming Zhou, Yinghua Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9798350362770;9798350362763

The existence of pests and diseases threatens the agricultural production and brings serious economic losses to countries whose economy is based on agriculture. Traditional agricultural production methods require a lot of manpower to identify and detect pests and diseases, which will increase the cost of agricultural production. Now, with the development of deep learning technology, computer vision based approaches can help reduce costs and improve efficiency. Through the deep learning model based on CNN, insects and pests can be accurately detected and recognized. This paper proposes an improved recognition model, MFnet, based on the lightweight deep neural network MobileNetV3. A new activation function, MLU6, is introduced in the proposed MFnet model. At the same time, we use the PolyLoss function to replace the cross-entropy loss function when training the proposed model. We also use transfer learning to improve the efficiency of network optimization and prevent overfitting through data augmentation and regularization. Ablation and comparative experiments are carried out and the IP102 pest dataset is used in the experiments. The experimental result shows that MFnet outperforms the original Mobilenetv3 and the other classic models in terms of classification accuracy, at the cost of increased number of parameters compared to the original Mobilenetv3. However, the parameter number, training time and classification time of the proposed model are still much less than those of the other classic models.

关键词： agriculture pest recognition computer vision image classification convolutional neural networks

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Research on ECO-HC Target Tracking Algorithm Based on Adaptive Template Update and Multi-Feature Fusion 6

Research on ECO-HC Target Tracking Algorithm Based on Adapti...

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6th International conference on Robotics and computer vision, ICRCV 2024

作者： Zhao, Yongsheng Yang, Luyang Liu, Ting Gao, Yuxing School of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798331527426

Aiming at the problem that it is difficult to fully and effectively utilize features for complete representation of target information during target tracking in complex scenes, this paper proposes an ECO-HC target tracking algorithm based on adaptive template updating and multi-feature fusion. In terms of template updating, an adaptive template mechanism is introduced, which dynamically determines whether the target is occluded or deformed by fusing color correlation and hash correlation, thus adjusting the template updating strategy to avoid template contamination. In terms of feature extraction, average filtered local binary pattern (AVLBP) features are proposed and fused with HOG features to enhance the algorithm's ability to capture the detailed texture of the target. Experiments on the OTB-100 tracking datasets show that the accuracy and success rate of this paper's algorithm on the datasets are 86.7% and 79.7%, respectively, which are improved by 2.9% and 2.7%, respectively, compared to the baseline method. The effectiveness of the algorithm is verified through qualitative and quantitative analysis, which demonstrates that the adaptive template updating and multi-feature fusion strategies can significantly improve the robustness and accuracy of target tracking. © 2024 ieee.

关键词： Local binary pattern

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DOTA: Deformable Optimized Transformer Architecture for End-to-End Text recognition with Retrieval-Augmented Generation 17

DOTA: Deformable Optimized Transformer Architecture for End-...

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17th International conference on Knowledge and Smart Technology, KST 2025

作者： Nithisopa, Naphat Panboonyuen, Teerapong Motor AI Recognition Solution (MARS) Thailand

ISBN: (纸本)9798331520403

Text recognition in natural images remains a challenging yet essential task, with broad applications spanning computer vision and natural language processing. This paper introduces a novel end-to-end framework that combines ResNet and vision Transformer backbones with advanced methodologies, including Deformable Convolutions, RetrievalAugmented Generation, and Conditional Random Fields (CRF). These innovations collectively enhance feature representation and improve Optical Character recognition (OCR) performance. Specifically, the framework substitutes standard convolution layers in the third and fourth blocks with Deformable Convolutions, leverages adaptive dropout for regularization, and incorporates CRF for more refined sequence modeling. Extensive experiments conducted on six benchmark datasets-IC13, IC15, SVT, IIIT5K, SVTP, and CUTE80-validate the proposed method's efficacy, achieving notable accuracies: 97.32% on IC13, 58.26% on IC15, 88.10% on SVT, 74.13% on IIIT5K, 82.17% on SVTP, and 66.67% on CUTE80, resulting in an average accuracy of 77.77%. These results establish a new state-of-the-art for text recognition, demonstrating the robustness of the approach across diverse and challenging datasets. © 2025 ieee.

关键词： component Conditional Random Fields (CRF) Deformable Convolutions natural images Optical Character recognition (OCR) ResNet Retrieval Augmented Generation Text recognition vision Transformer

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Recent Advances in Human Activity recognition Techniques: A Comprehensive Review 3

Recent Advances in Human Activity Recognition Techniques: A ...

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3rd International conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Saha, Sukanya Banik, Debapriya ICFAI University Tripura Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798331523893

Human Activity recognition (HAR) interprets data from healthcare, security surveillance, human-computer interaction (HCI), and sensors to recognize and categorize human actions. The rapid development of artificial intelligence (AI) and the widespread use of sensors have drawn great interest and demonstrated the immense application potential of HAR systems based on non-invasive ambient sensors. Various methods for performing HAR have been reported by researchers. In this survey, it has been noticed that data can be collected in vision and sensor-based formats. The goal of video-based HAR is to automatically identify actions in video clips. Smart wearable sensors are the most popular kind of sensor-based HAR. Surveys have identified HAR tendencies across many contexts and applications. The researchers classified HAR techniques into three categories: online versus offline, multimodal versus unimodal, and feature-based versus learning-based approaches. This survey has focused on the recognition of different activities like sports, yoga, different dance poses, human interaction, and human actions. This survey also concentrated on single-view and multiview datasets. This study compares and contrasts the most recent HAR techniques with a discussion of widely used datasets. Various criteria, including dataset size and assessment metrics, were used to categorize and analyze the chosen studies. This survey also focused on different dataset availability. © 2025 ieee.

关键词： computer vision

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New Methods for Animal Detection in Camera Trap Image Sequences 12

New Methods for Animal Detection in Camera Trap Image Sequen...

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ieee Intelligent Mobile Computing (MobileCloud)

作者： Wang, Yizhen Wang, Junlin Shang, Yi Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65212 USA

ISBN: (纸本)9798331539856

Nowadays, camera traps are widely employed in monitoring biodiversity and assessing the population density of animal species. A challenge in animal recognition in camera trap images is the detection of small animals in complex environments and the identification of heavily obscured animals. This paper presents two novel methods that leverage sequentially captured images to improve animal recognition accuracy: one utilizing optical flow information and the other a motion-based algorithm based on the principle of median filtering. In experiments, we used two new real-world sequence-based camera trap image datasets to evaluate these methods. Our findings indicate that optical flow information effectively reduces false positive cases, while the motion-based algorithm significantly improves the accuracy of detecting animal presence and counting by substantially reducing false negative cases. Specifically, using the MegaDetector with a confidence threshold of 0.5 as the baseline, the motion-based method reduced false negative cases by over 70% while only slightly increasing false positive cases, and improved animal counting accuracy by more than 25%.

关键词： camera traps animal detection in images optical flow deep learning computer vision

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Research on intelligent insect species identification based on computer vision 7

Research on intelligent insect species identification based ...

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7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Huang, Bin Nanchang Institute of Technology Jiangxi Nanchang330044 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

Image-based insect species identification is a comprehensive application of computer vision technology, image processing technology and pattern recognition technology to realize insect species identification. It is of great significance to find and identify crop insects in time and quickly, so as to take corresponding measures to prevent and control pests in time and reduce the losses caused by pests. In this paper, intelligent insect species identification based on computer vision is studied. For insect images, using feature fusion technology will obtain more comprehensive insect image features, which will greatly improve the recognition accuracy and improve the recognition efficiency of the system. In the learning process, firstly, a certain number of insect images are selected as training samples, and the feature vectors of insects are extracted as the input vectors of SVM(support vector machine) training, and the input vectors are learned to get the best classification model. The experimental results show that the average recognition rate of insects by feature fusion is 94.747%. The experimental results show that the research results of this paper provide theoretical and practical basis for intelligent insect species recognition technology based on computer vision. © 2023 SPIE. All rights reserved.

关键词： Support vector machines

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A Hybrid Approach Integrating Deep Learning and Knowledge Graph for Detection and recognition of Machine Parts 2

A Hybrid Approach Integrating Deep Learning and Knowledge Gr...

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2nd ieee Global conference on Communications and Information Technologies, GCCIT 2024

作者： Kulkarni, Vaishnavi V. Kundu, Mayuri Simha, J.B. Reva University School of Computer Science & Engineering Bengaluru India Bengaluru India

ISBN: (纸本)9798350388916

While machine learning powers impressive computer vision systems, they lack the human advantage of general world knowledge. This means they struggle to interpret visual data with humans' same richness of understanding. Accurate machine part detection is critical for intelligent manufacturing and automation. This paper employs a drilling machine as an example to demonstrate the approach to machine part identification. The model uses a specialized computer vision technique called Named Part recognition (NPR) to recognize image components even with minimal training data. The model shows impressive accuracy in identifying parts of a drilling machine. Detecting machine parts is pivotal for identifying faulty components and averting malfunctions. It consistently detects the correct parts with a success rate between 89.6% and 92.5% (precision). Additionally, it rarely mistakes other objects for parts (high specificity exceeding 95%). This balanced performance is further highlighted by the overall accuracy consistently hovering around 95.5%. © 2024 ieee.

关键词： Adjacency Matrix Knowledge Graph Machine Part Detection You Only Look Once (YOLO)

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Perusal of Camera Trap Sequences Across Locations 10th

Perusal of Camera Trap Sequences Across Locations

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11th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Banerjee, Anoushka Dinesh, Dileep Aroor Bhavsar, Arnav Indian Inst Technol Mandi MANAS Lab SCEE Kamand HP India

ISBN: (纸本)9783031245374;9783031245381

The current rate of decline in biodiversity exclaims ecological conservation. In response, camera traps are being increasingly deployed for the perlustration of wildlife. The analyses of camera trap data can aid in curbing species extinction. However, a substantial amount of time is lost in the manual review curtailing the usage of camera traps for prompt decision-making. The insuperable visual challenges and proneness of camera trap to record empty frames (frames that are natural backdrops with no wildlife presence) deem wildlife detection and species recognition a demanding and taxing task. Thus, we propose a pipeline for wildlife detection and species recognition to expedite the processing of camera trap sequences. The proposed pipeline consists of three stages: (i) empty frame removal, (ii) wildlife detection, and (iii) species recognition and classification. We leverage vision transformer (ViT), DEtection TRansformer (DETR), vision and detection transformer (ViDT), faster region based convolutional neural network (Faster R-CNN), inception v3, and ResNet 50 for the same. We examine the adroitness of the leveraged algorithms at new and unseen locations against the challenges of domain generalisation. We demonstrate the effectiveness of the proposed pipeline using the Caltech camera trap (CCT) dataset.

关键词： Camera traps Empty frame removal Wildlife detection Wildlife species classification Domain generalisation DEtection TRansformer (DETR) vision transformer (ViT) vision and detection transformers (ViDT) Inception v3

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NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces

NewtonianVAE: Proportional Control and Goal Identification f...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jaques, Miguel Burke, Michael Hospedales, Timothy Univ Edinburgh Edinburgh Midlothian Scotland Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

Learning low-dimensional latent state space dynamics models has proven powerful for enabling vision-based planning and learning for control. We introduce a latent dynamics learning framework that is uniquely designed to induce proportional controlability in the latent space, thus enabling the use of simple and well-known PID controllers. We show that our learned dynamics model enables proportional control from pixels, dramatically simplifies and accelerates behavioural cloning of vision-based controllers, and provides interpretable goal discovery when applied to imitation learning of switching controllers from demonstration. Notably, such proportional controlability also allows for robust path following from visual demonstrations using Dynamic Movement Primitives in the learned latent space.

关键词： Visualization computer vision Proportional control Cloning Switches Aerospace electronics Planning

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