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

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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...

引用

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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Hierarchical Unified Spectral-Spatial Aggregated Transformer for Hyperspectral Image Classification 26

Hierarchical Unified Spectral-Spatial Aggregated Transformer...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Zhou, Weilian Kamata, Sei-Ichiro Luo, Zhengbo Chen, Xiaoyue Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

vision Transformer (ViT) has recently been introduced into the computer vision (CV) field with its self-attention mechanism and gotten remarkable performance. However, simply applying ViT for hyperspectral image (HSI) classification is not applicable due to 1) ViT is a spatial-only self-attention model, but rich spectral information exists in HSI;2) ViT needs sufficient training samples, but HSI suffers from limited samples;3) ViT does not well learn local features;4) multi-scale features for ViT are not considered. Furthermore, the methods which combine convolutional neural network (CNN) and ViT generally suffer from a large computational burden. Hence, this paper tends to design a suitable pure ViT based model for HSI classification as the following points: 1) spectral-only vision transformer with all tokens' aggregation;2) spatial-only local-global transformer;3) cross-scale local-global feature fusion, and 4) a cooperative loss function to unify the spectral and spatial features. As a result, the proposed idea achieves competitive classification performance on three public datasets than other state-of-the-art methods.

关键词： Training computer vision Computational modeling Transformers pattern recognition Convolutional neural networks Information exchange

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Audio Transformer for Synthetic Speech Detection via Multi-Formant Analysis

Audio Transformer for Synthetic Speech Detection via Multi-F...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Luca Cuccovillo Milica Gerhardt Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel multi-task transformer for detecting synthetic speech. The network encodes magnitude and phase of the input speech with a feature bottleneck, used to autoencode the input magnitude, to predict the trajectory of the first phonetic formants (F0, F1, F2), and to distinguish whether the input speech is synthetic or natural. The approach achieves state-of-the-art performance on the ASVspoof 2019 LA dataset with an AUC score of 0.932, while ensuring interpretability at the same time.

关键词： computer vision conferences Phonetics Transformers Multitasking Trajectory pattern recognition

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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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Perusal of Camera Trap Sequences Across Locations 10th

Perusal of Camera Trap Sequences Across Locations

引用

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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New Methods for Animal Detection in Camera Trap Image Sequences 12

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

引用

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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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...

引用

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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Shadow-Mapping for Unsupervised Neural Causal Discovery

Shadow-Mapping for Unsupervised Neural Causal Discovery

引用

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

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit 'mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.

关键词： computer vision Correlation conferences Neural networks Estimation pattern recognition Dynamical systems

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Differentiable Patch Selection for Image recognition

Differentiable Patch Selection for Image Recognition

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

作者： Cordonnier, Jean-Baptiste Mahendran, Aravindh Dosovitskiy, Alexey Weissenborn, Dirk Uszkoreit, Jakob Unterthiner, Thomas Ecole Polytech Fed Lausanne Lausanne Switzerland Google Res Brain Team Mountain View CA USA

ISBN: (纸本)9781665445092

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high resolution images. Our method may be interfaced with any downstream neural network, is able to aggregate information from different patches in a flexible way, and allows the whole model to be trained end-to-end using backpropagation. We show results for traffic sign recognition, inter-patch relationship reasoning, and fine-grained recognition without using object/part bounding box annotations during training.

关键词： Training computer vision Image resolution Image recognition Computational modeling Neural networks Memory management

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

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

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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