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检索条件"机构=Computer vision and Image processing lab"

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Improved YOLOv5 Algorithm for Small Object Detection in Drone images 12th

Improved YOLOv5 Algorithm for Small Object Detection in Dr...

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12th International Conference on Computational Visual Media, CVM 2024

作者： Lin, Yitong Liu, Yiguang Vision and Image Processing Lab College of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9789819720910

The object detection in the context of drone is a hot topic in the field of computer vision in recent years. In response to the challenge of limited image feature information and the presence of numerous small and densely packed objects in drone-captured images, this paper proposes a novel feature fusion detection model, HTH-YOLOv5, based on YOLOv5. Firstly, we enhance the detection capability of small objects by adding a detection channel from high-resolution feature maps and propose a Hybrid Transformer Head (HTH) that incorporates a hybrid Transformer module, aiming to improve the network’s focus on small objects by fusing global and local feature information. Secondly, we introduce a Convolutional Attention Feature Fusion module (CA-FF) based on CBAM. This module dynamically adjusts attention weights for the allocation of original feature maps in both channel and spatial dimensions, aiming to enhance the feature extraction capability for small objects. Finally, to better capture global and contextual information, we introduce the Hybrid Transformer module into the backbone and enhance its original feature fusion method using the CA-FF module. Experiments on the Vis-Drone 2021 dataset demonstrate that, compared to the baseline YOLOv5s model, the improved model shows an increase of 7.2% in mAP50 and 6.3% in mAP75. The model trained with an input resolution of 1540×1540 achieves an mAP50 of 57.1%, marking a 12.4% improvement over YOLOv5. The improved HTH-YOLOv5 achieves increased accuracy while maintaining a detection speed of 45 FPS, making it more suitable for small object detection in drone scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drones

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Multiview Human Action Recognition System Based on OpenPose and KNN Classifier 11th

Multiview Human Action Recognition System Based on OpenPose ...

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11th International Conference on Robotics, vision, Signal processing and Power Applications, RoViSP 2021

作者： Malik, Najeeb Ur Rehman Abu Bakar, Syed Abdul Rahman Sheikh, Usman Ullah Computer Vision Video and Image Processing Lab ECE Department Universiti Teknologi Malaysia Johor Malaysia

ISBN: (纸本)9789811681288

Human action recognition is one of the trending research topics in the field of computer vision. Human-computer interaction and video monitoring are broad applications that aid in the understanding of human action in a video. The problem with action recognition algorithms such as 3D CNN, Two-stream network, and CNN-LSTM is that they have highly complex models including a lot of parameters resulting in difficulty while training them. Such models require high configuration machines for real-time human action recognition. Therefore, present research proposes the use of 2D skeleton features along with a KNN classifier based HAR system to overcome the aforementioned problems of complexity and response time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Hierarchical Attention Approach in Multimodal Emotion Recognition for Human Robot Interaction 36

Hierarchical Attention Approach in Multimodal Emotion Recogn...

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36th International Technical Conference on Circuits/Systems, computers and Communications, ITC-CSCC 2021

作者： Abdullah, Muhammad Ahmad, Mobeen Han, Dongil Vision and Image Processing Lab Sejong University Department of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9781665435536

The ability to perceive human emotions is one of the key elements that may promise a natural, genuine and more reliable human robot interaction. Though emotional perception in human robot interaction has been challenged by many difficulties, the lack of contextual understanding is can be attributed as the biggest hurdle in this regard. Most of the literature refers to the datasets developed in controlled environment to validate the performance of their systems which happens to be really good. Still those systems are very far from achieving that kind of performance in real-life scenarios. In this paper a multimodal emotion recognition strategy is presented, that uses voice features in addition to the facial expressions to determine the emotional state of the user. A hierarchical attention layer is used to for feature fusion purpose. The final system is end-to-end trainable, multimodal approach makes it more resilient to the changes in environment. The achieved 76.3% accuracy for eNterface05 video dataset, which is higher than the any single modality approach in the comparison. © 2021 IEEE.

关键词： Emotion Recognition

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Is there progress in activity progress prediction?

Is there progress in activity progress prediction?

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Frans de Boer Jan C. van Gemert Jouke Dijkstra Silvia L. Pintea Computer Vision Lab Delft University of Technology Division of Image Processing (LKEB) Leiden University Medical Center

Activity progress prediction aims to estimate what percentage of an activity has been completed. Currently this is done with machine learning approaches, trained and evaluated on complicated and realistic video datasets. The videos in these datasets vary drastically in length and appearance. And some of the activities have unanticipated developments, making activity progression difficult to estimate. In this work, we examine the results obtained by existing progress prediction methods on these datasets. We find that current progress prediction methods seem not to extract useful visual information for the progress prediction task. Therefore, these methods fail to exceed simple frame-counting baselines. We design a precisely controlled dataset for activity progress prediction and on this synthetic dataset we show that the considered methods can make use of the visual information, when this directly relates to the progress prediction. We conclude that the progress prediction task is ill-posed on the currently used real-world datasets. Moreover, to fairly measure activity progression we advise to consider a, simple but effective, frame-counting baseline.

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Entropy: A New Parameter for image Deciphering

Entropy: A New Parameter for Image Deciphering

引用

International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020

作者： Cheggoju, Naveen Nawandar, Neha K. Satpute, Vishal R. Image Processing and Computer Vision Lab Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789811572333

Data and information, these two terms may look quite similar and also sometimes used with the same meaning. But in-depth, these terms are completely different from each other. This paper mainly focuses on this difference to extract the encrypted information from the data (importantly image data). Here, every aspect of the study and the way of formulation are explained using "Einstein’s theory of relativity" and "Arrow of time" for better understanding. Einstein’s theory is used to explain "how similar things can be seen in different ways?" (here data and information) and arrow of time is used to explain "how the correct information can be extracted from the data?". To explain this concept, an image encrypted using a chaotic logistic map is used. The edge information present in this data is taken as the parameter to differentiate the unorganized data and organized data (correct information). Here, a new parameter called "Randomness parameter (Rp)" is introduced, which gives the entropy (randomness) of the data. The outcome of this parameter is used to differentiate the data and correct information. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Entropy

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

引用

14th International Conference on Digital image processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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image saliency detection method based on multi-feature maps fusion 4

Image saliency detection method based on multi-feature maps ...

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4th International Conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Li, Xiaoli Liu, Yunpeng Zhao, Huaici Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510683242

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics." © 2024 SPIE.

关键词： Feature extraction

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A step towards understanding why classification helps regression

A step towards understanding why classification helps regres...

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International Conference on computer vision (ICCV)

作者： Silvia L. Pintea Yancong Lin Jouke Dijkstra Jan C. van Gemert Computer Vision Lab Delft University of Technology Division of Image Processing (LKEB) Leiden University Medical Center Intelligent Vehicles Group Delft University of Technology

A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so, we start from precisely controlled dataset variations and data samplings and find that the effect of adding a classification loss is the most pronounced for regression with imbalanced data. We explain these empirical findings by formalizing the relation between the balanced and imbalanced regression losses. Finally, we show that our findings hold on two real imbalanced image datasets for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on the problem of imbalanced video progress prediction (Breakfast). Our main takeaway is: for a regression task, if the data sampling is imbalanced, then add a classification loss.

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A Simplified Skeleton Joints Based Approach For Human Action Recognition 7

A Simplified Skeleton Joints Based Approach For Human Action...

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7th IEEE International Conference on Signal and image processing Applications, ICSIPA 2021

作者： Malik, Najeeb Ur Rehman Abu Bakar, Syed A.R. Sheikh, Usman Ullah Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia UTM Johor Bahru Campus Johor 81310 Malaysia

ISBN: (纸本)9781665435925

The growing technological development in the field of computer vision in general, and human action recognition (HAR), in particular, have attracted increasing number of researchers from various disciplines. Amid the variety of challenges in the field of human action recognition, one of the major issues is complex modelling which requires multiple parameters leading to troublesome training which further requires heavy configuration machines for real-time recognition. Therefore, there is a need to develop a simplified method that could result in reduced complexity, without compromising the performance accuracy. In order to address the mentioned issue, this paper proposes an approach that extracts the mean, variance and median from the skeleton joint locations and directly uses them in the classification process. The system used MCAD dataset for extracting 2D skeleton features with the help of OpenPose technique, which is suitable for the extraction of skeleton features from the 2D image instead of 3D image or using a depth sensor. Henceforth, we avoid using either the RGB images or the skeleton images in the recognition process. The method shows a promising performance with an accuracy of 73.8% when tested with MCAD dataset. © 2021 IEEE

关键词： image processing

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Learning Robust Models Using the Principle of Independent Causal Mechanisms 43rd

Learning Robust Models Using the Principle of Independent ...

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43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Müller, Jens Schmier, Robert Ardizzone, Lynton Rother, Carsten Köthe, Ullrich Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Computer Vision and Learning Lab Heidelberg University Heidelberg Germany Bosch Center for Artificial Intelligence Renningen Germany

ISBN: (纸本)9783030926588

Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, [31]) can turn this weakness into an opportunity: one can take advantage of distribution shift between different environments during training in order to obtain more robust models. We propose a new gradient-based learning framework whose objective function is derived from the ICM principle. We show theoretically and experimentally that neural networks trained in this framework focus on relations remaining invariant across environments and ignore unstable ones. Moreover, we prove that the recovered stable relations correspond to the true causal mechanisms under certain conditions, turning domain generalization into a causal discovery problem. In both regression and classification, the resulting models generalize well to unseen scenarios where traditionally trained models fail. © 2021, Springer Nature Switzerland AG.

关键词： Learning systems

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