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

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Object Detection and Action recognition using computer vision

Object Detection and Action Recognition using Computer Visio...

引用

2023 International conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Siva Swetha Reddy, M.S. Khatravath, Prem Rathod Surineni, Nithin Kumar Mulinti, Kedarnath Reddy Institute of Aeronautical Engineering Dundigal Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350333602

One of the main issues with computer vision is the recognition of objects and actions. Deep learning has significantly changed how society uses artificial intelligence since it first emerged a few years ago. The study was primarily designed for monitoring and proctoring reasons. Action recognition is used to keep track of the subject's motions, and object detection is used to locate objects in the scene. This proposed model is built in Python using real-time computer vision frameworks like Open CV and YOLO. Moreover, Open CV is used in image processing and machine learning. Up to 80 different things can be recognized from the data set of objects provided. You Only Look Once(YOLO), Faster Recurrent Convolutional Neural Networks (RCNN), and Single Shot Detector (SSD). When performance is more important than accuracy, YOLO excels while Faster RCNN and SSD do better. This technique efficiently detects objects without degrading performance. There are few challenges faced during the study auch as Viewpoint Variation, Deformation, Occlusion, Illumination Condition, Cluttered, Intra-Class Variation. © 2023 ieee.

关键词： computer vision

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Capsule Network is Not More Robust than Convolutional Network

Capsule Network is Not More Robust than Convolutional Networ...

引用

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

作者： Gu, Jindong Tresp, Volker Hu, Han Univ Munich Munich Germany Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet affect its robustness. In this paper, we first carefully examine the special designs in CapsNet that differ from that of a ConvNet commonly used for image classification. The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization. Along with these major differences, we conduct comprehensive ablation studies on three kinds of robustness, including affine transformation, overlapping digits, and semantic representation. The study reveals that some designs, which are thought critical to CapsNet, actually can harm its robustness, i.e., the dynamic routing layer and the transformation process, while others are beneficial for the robustness. Based on these findings, we propose enhanced ConvNets simply by introducing the essential components behind the CapsNet's success. The proposed simple ConvNets can achieve better robustness than the CapsNet.

关键词： computer vision Aggregates Semantics Routing Robustness pattern recognition Task analysis

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Advancing Emotion recognition in Visual Data with Artificial Intelligence

Advancing Emotion Recognition in Visual Data with Artificial...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Anusree, K. Bhuvaneshvari, B. Balakrishnan, D. Nandhagopal, S. Chellakannu, Aarthy Logeshwari, S. K S R Institute for Engineering and Technology Department of Information Technology Namakkal India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India K P R Institute of Engineering and Technology Department of Computer Science Engineering Coimbatore India Dhanalakshmi Srinivasan University Department of Computer Science & Engineering Tiruchirappalli India

ISBN: (纸本)9798350359688

The burgeoning discipline of affective computing, which sits at the nexus of AI and psychology, aims to improve our capacity to comprehend and analyze human emotions as they manifest themselves in visual data. This abstract provides a succinct overview of the problem, highlighting the key elements that characterize the discipline's significance and ramifications. Emotion recognition in visual data requires sophisticated artificial intelligence techniques, including computer vision algorithms and deep learning models. These technologies have made it feasible to accurately deduce emotional states from facial expressions, body language, and even speech sentiment. Facial expression analysis has gained popularity in particular because it may be used to recognize a variety of emotions © 2024 ieee.

关键词： Convolutional neural networks

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Stable View Synthesis

Stable View Synthesis

引用

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

作者： Riegler, Gernot Koltun, Vladlen Intel Labs Hillsboro OR 97124 USA

ISBN: (纸本)9781665445092

We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view rays and corresponding feature vectors that encode the appearance of this point in the input images. The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view. The target view is then rendered by a convolutional network from a tensor of features synthesized in this way for all pixels. The method is composed of differentiable modules and is trained end-to-end. It supports spatially-varying view-dependent importance weighting and feature transformation of source images at each point;spatial and temporal stability due to the smooth dependence of on-surface feature aggregation on the target view;and synthesis of view-dependent effects such as specular reflection. Experimental results demonstrate that SVS outperforms state-of-the-art view synthesis methods both quantitatively and qualitatively on three diverse real-world datasets, achieving unprecedented levels of realism in free-viewpoint video of challenging large-scale scenes.

关键词： Convolutional codes computer vision Three-dimensional displays Tensors Reflection pattern recognition

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SwiftNet: Real-time Video Object Segmentation

SwiftNet: Real-time Video Object Segmentation

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

作者： Wang, Haochen Jiang, Xiaolong Ren, Haibing Hu, Yao Bai, Song Alibaba Youku Cognit & Intelligent Lab Beijing Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision.

关键词： computer vision Codes Redundancy Memory management Object segmentation Streaming media Real-time systems

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One-Stage Open-Vocabulary Temporal Action Detection Leveraging Temporal Multi-scale and Action Label Features 18

One-Stage Open-Vocabulary Temporal Action Detection Leveragi...

引用

18th International conference on Automatic Face and Gesture recognition (FG)

作者： Nguyen, Trung Thanh Kawanishi, Yasutomo Komamizu, Takahiro Ide, Ichiro Nagoya Univ Grad Sch Informat Nagoya Aichi 4648601 Japan RIKEN Guardian Robot Project Informat R&D & Strategy Headquarters Seika Kyoto 6190288 Japan Nagoya Univ Math & Data Sci Ctr Nagoya Aichi 4648601 Japan

ISBN: (纸本)9798350394948;9798350394955

Open-vocabulary Temporal Action Detection (Open-vocab TAD) is an advanced video analysis approach that expands Closed-vocabulary Temporal Action Detection (Closedvocab TAD) capabilities. Closed-vocab TAD is typically confined to localizing and classifying actions based on a predefined set of categories. In contrast, Open-vocab TAD goes further and is not limited to these predefined categories. This is particularly useful in real-world scenarios where the variety of actions in videos can be vast and not always predictable. The prevalent methods in Open-vocab TAD typically employ a 2-stage approach, which involves generating action proposals and then identifying those actions. However, errors made during the first stage can adversely affect the subsequent action identification accuracy. Additionally, existing studies face challenges in handling actions of different durations owing to the use of fixed temporal processing methods. Therefore, we propose a 1-stage approach consisting of two primary modules: Multi-scale Video Analysis (MVA) and Video-Text Alignment (VTA). The MVA module captures actions at varying temporal resolutions, overcoming the challenge of detecting actions with diverse durations. The VTA module leverages the synergy between visual and textual modalities to precisely align video segments with corresponding action labels, a critical step for accurate action identification in Open-vocab scenarios. Evaluations on widely recognized datasets THUMOS14 and ActivityNet-1.3, showed that the proposed method achieved superior results compared to the other methods in both Open-vocab and Closed-vocab settings. This serves as a strong demonstration of the effectiveness of the proposed method in the TAD task.

关键词： computer vision

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Target Detection Algorithm for Foggy Scenes Based on Twin Network 6

Target Detection Algorithm for Foggy Scenes Based on Twin Ne...

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6th ieee International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Cai, Liming Yu, Yuecheng Ning, Anqi Shi, Jinlong Chen, Xudong Huang, Shixin Jiangsu University of Science and Technology School of Computer Science Zhenjiang China Co. Ltd Zhenjiang China

ISBN: (纸本)9798350325485

In real scenes, the quality and contrast of images are reduced due to the uneven state of haze caused by factors such as humidity, dust, and aerosols in the air. In this scenario, it is difficult for a general detection network to detect targets in the image. Thus, a dual subnet based on multi-Task collaborative training (DSMCT) is proposed. Firstly, during the training stage, GCANet is used as the supervisory network for YOLOX, which promotes the extraction of clean information by YOLOX in foggy scenes. During the testing stage, only the YOLOX branch is activated to ensure the detection speed of the model. Secondly, the deformable convolution module is used to improve the gated context aggregation network (GCANet), enhancing the model's ability to capture details of non-homogeneous fog. Finally, we introduce the Coordinate Attention mechanism into the vision Transformer and redesign the backbone network of YOLOX to enhance the network's feature extraction capability for deep semantic information. The mAP of DSMCT reached 86.56% on the artificially hazy dataset (FOG-VOC), and it reached 62.39% on the real hazy dataset (RTTS). This is respectively 2.27% and 4.41% higher than the current state-of-The-Art detection models. The experiments show that the improved DSMCT network has high practicality and effectiveness for object detection in real hazy scenes. © 2023 ieee.

关键词： Object detection

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LASR: Learning Articulated Shape Reconstruction from a Monocular Video

LASR: Learning Articulated Shape Reconstruction from a Monoc...

引用

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

作者： Yang, Gengshan Sun, Deqing Jampani, Varun Vlasic, Daniel Cole, Forrester Chang, Huiwen Ramanan, Deva Freeman, William T. Liu, Ce Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved great success in modeling the "closed world" of known object categories, they cannot well handle the "open-world" of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Our code is available at ***.

关键词： computer vision Three-dimensional displays Codes Shape Dogs Cameras pattern recognition

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Box-Level Tube Tracking and Refinement for Vehicles Anomaly Detection

Box-Level Tube Tracking and Refinement for Vehicles Anomaly ...

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

作者： Wu, Jie Wang, Xionghui Xiao, Xuefeng Wang, Yitong ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9781665448994

Traffic Anomaly detection is an essential computer vision task and plays a critical role in video structure analysis and urban traffic analysis. In this paper, we propose a box-level tracking and refinement algorithm to identify anomaly detection in road scenes. We first link the detection results to construct candidate spatio-temporal tubes via greedy search. Then the box-level refinement scheme is introduced to employ auxiliary detection cues to promote the abnormal predictions, which consists of spatial fusion, still-thing filter, temporal fusion, and feedforward optimization. Still-thing filter and feedforward optimization employ complementary detection concepts to promote the abnormal predictions, which helps determine an accurate abnormal period. The experimental results show that our approach is superior in the Traffic Anomaly Detection Track test set of the NVIDIA AI CITY 2021 CHALLENGE, which ranked second in this competition, with a 93.18% F1-score and 3.1623 root mean square error. It reveals that the proposed approach contributes to fine-grained anomaly detection in actual traffic accident scenarios and promoting the development of intelligent transportation.

关键词： computer vision Urban areas Transportation Electron tubes Artificial intelligence Vehicle dynamics Task analysis

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Deep Texture recognition via Exploiting Cross-Layer Statistical Self-Similarity

Deep Texture Recognition via Exploiting Cross-Layer Statisti...

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

作者： Chen, Zhile Li, Feng Quan, Yuhui Xu, Yong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9781665445092

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Aggregation of Statistical Self-similarity) for texture recognition. We model the CNN feature maps across different layers, as a dynamic process which carries the statistical self-similarity (SSS), one well-known property of texture, from input image along the network depth dimension. The CLASS module characterizes the cross-layer SSS using a soft histogram of local differential box-counting dimensions of cross-layer features. The resulting descriptor encodes both cross-layer dynamics and local SSS of input image, providing additional discrimination over the often-used global average pooling. Integrating CLASS into a ResNet backbone, we develop CLASSNet, an effective deep model for texture recognition, which shows state-of-the-art performance in the experiments.

关键词： Histograms computer vision Computational modeling Tools pattern recognition Convolutional neural networks

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