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

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Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Traffic Video Event Retrieval via Text Query using Vehicle A...

引用

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

作者： Tien-Phat Nguyen Ba-Thinh Tran-Le Xuan-Dang Thai Nguyen, Tam, V Do, Minh N. Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam John von Neumann Inst Ho Chi Minh City Vietnam Univ Dayton Dayton OH 45469 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query's attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.

关键词： Visualization computer vision Tracking conferences Urban areas Semantics pattern recognition

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Localized Triplet Loss for Fine-grained Fashion Image Retrieval

Localized Triplet Loss for Fine-grained Fashion Image Retrie...

引用

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

作者： D'Innocente, Antonio Garg, Nikhil Zhang, Yuan Bazzani, Loris Donoser, Michael Sapienza Univ Rome Rome Italy Amazon Munich Germany Amazon Seattle WA USA

ISBN: (纸本)9781665448994

Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query. However, global embeddings struggle to capture localized fine-grained similarities between images, because of aggregation operations. Our work deals with this problem by learning localized representations for fashion retrieval based on local interest points of prominent visual features specified by a user. We introduce a localized triplet loss function that compares samples based on corresponding patterns. We incorporate random local perturbation on the interest point as a key regularization technique to enforce local invariance of visual representations. Due to the absence of existing fashion datasets to train on localized representations, we introduce FashionLocalTriplets, a new high-quality dataset annotated by fashion specialists that contains triplets of women's dresses and interest points. The proposed model outperforms state-of-the-art global representations on FashionLocalTriplets.

关键词： Visualization computer vision Annotations Perturbation methods conferences Image retrieval pattern recognition

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Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Deep Fusion of Appearance and Frame Differencing for Motion ...

引用

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

作者： Ellenfeld, Marc Moosbauer, Sebastian Cardenes, Ruben Klauck, Ulrich Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Hensoldt Analyt GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany Univ Western Cape Cape Town South Africa

ISBN: (纸本)9781665448994

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene's motion independent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.

关键词： computer vision Image segmentation Three-dimensional displays Fuses Motion segmentation Cameras pattern recognition

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Haessig, Germain Joubert, Damien Haque, Justin Milde, Moritz B. Delbruck, Tobi Gruev, Viktor Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria University of Zurich Institute of Neuroinformatics ETH Zurich Switzerland Western Sydney University International Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350302493

The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired frame-based cameras lack the high dynamic range and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ~12ms and dynamic range to ~72dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with submillisecond latencies over a millionfold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low-cost and low-latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image high dynamic range polarization scenes that vary at high speeds and to observe the dynamical properties of single collagen fibers in a bovine tendon under rapid cyclical ***: https://***/mFuCeTMWEqY © 2023 ieee.

关键词： Polarization

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3D Spatial recognition without Spatially Labeled 3D

3D Spatial Recognition without Spatially Labeled 3D

引用

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

作者： Ren, Zhongzheng Misra, Ishan Schwing, Alexander G. Girdhar, Rohit Facebook AI Res Menlo Pk CA 94025 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781665445092

We introduce WyPR, a Weakly-supervised framework for Point cloud recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. We show that in conjunction with standard multiple-instance learning objectives, WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time. We demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming prior state of the art on weakly-supervised segmentation by more than 6% mIoU. In addition, we set up the first benchmark for weakly-supervised 3D object detection on both datasets, where WyPR outperforms standard approaches and establishes strong baselines for future work.

关键词： Training Couplings computer vision Three-dimensional displays Semantics Object detection pattern recognition

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Big Data Intelligence Empowered Specialized Disciplines Development pattern recognition in Power Industry Universities 5th

Big Data Intelligence Empowered Specialized Disciplines Deve...

引用

5th International conference on Big Data and Security (ICBDS)

作者： Zhang, Yan Liu, PengCheng Xu, Hao Ge, Linlin Zhu, Zhentao Nanjing Inst Technol Sch Econ & Management Nanjing 211167 Peoples R China Nanjing Inst Technol Sch Elect Engn Nanjing 211167 Peoples R China Nanjing Univ Sch Informat Management Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9789819743865;9789819743872

Universities are concentrating on building industry characteristics to strengthen and expand their influence in the information age. Big data on intellectual output is a key representation of discipline construction. We created an algorithm to identify development features of disciplines, including temporal trend, research hotspots, and mutation characteristics. Using CNKI as data source, with the aid of scientific knowledge graph and social network analysis, 10786 core journal thesis published between 2012 and 2021 demonstrated the co-occurrence, keyword development trajectory, and mutation word development path. According to data mining, high-level intellectual output that is relevant to industry increased in quantity and proportion, and intimacy also improved. High levels of interdisciplinary interaction, a variety of disciplinary innovations, and in-depth disciplinary culture formation should characterize the pattern of disciplinary development. This study is an attempt of specialized disciplines development pattern recognition by big data intelligence, and the recognition algorithms can be used for feature recognition in multidisciplinary fields.

关键词： Big data mining and visualization Big Data Intelligence Social network construction pattern recognition Specialized Disciplines in Power Industry Universities

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INSIGHTS INTO OBJECT recognition: A COMPARATIVE STUDY OF FEATURE DETECTION AND MATCHING ALGORITHMS USING KINECT DATA 15

INSIGHTS INTO OBJECT RECOGNITION: A COMPARATIVE STUDY OF FEA...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Narayanan, Krishnammal Lakshmi, G. Muthu Vasumathy, M. Department of computer science and Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology avadi-55 India Department of computer science and engineering Manonmaniam Sundaranar University Tirunelveli India

ISBN: (纸本)9798350370249

In today's rapidly evolving technological landscape, object recognition is a critical component of computer vision, impacting numerous fields such as robotics, surveillance, and augmented reality. Object recognition involves identifying and classifying semantic objects within digital images and videos. A fundamental aspect of this process is feature detection, which is essential for accurate recognition and matching. This paper presents a comparative analysis of various image matching algorithms, categorized into template-based, texture feature-based, and intensity-based methods. Specifically, we examine the performance of Edge-Oriented Gradient (EOG) for template-based matching, Speeded-Up Robust Features (SURF) for texture-based matching, and entropy and mean for intensity-based matching. Experimental results demonstrate the efficacy of these algorithms in detecting and matching features under different transformations and deformations. Our study evaluates each algorithm's matching accuracy and computational efficiency, identifying the optimal approach for specific object recognition scenarios. ©2024 ieee.

关键词： Template matching

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Detection and recognition of Vehicles Using Indian Driving Datasets 11

Detection and Recognition of Vehicles Using Indian Driving D...

引用

11th ieee International conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Padukote, Yathish H.S. Suresha, R. Manohar, N. Jipeng, Tian Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Computer Science college Zhongyuan University of Technology Henan Province Zhengzhou China

ISBN: (纸本)9798350360059

Vehicle identification and recognition are essential computer vision tasks with important applications in autonomous driving, traffic management, and surveillance systems. The Indian Driving Dataset (IDD) dataset used in this work was compiled from a variety of driving scenarios in the Indian environment, and it includes a thorough investigation on vehicle detection and recognition. The dataset is meticulously chosen to reflect the difficulties brought on by the state of Indian roads, traffic patterns, and vehicle kinds. In this study, a Faster RCNN method for precise and effective vehicle detection and recognition deep learning approaches by enhancing images using power-law transformation. The collection consists of annotated IDD dataset images taken in a range of lighting situations, urban-rural locales, and camera angles. The testing results demonstrate the effectiveness of the recommended approach, which achieves competitive performance with 79.5% detection accuracy and 76% recognition accuracy. Additionally, the performance of the model is impacted by elements like various lighting situations and various vehicle models. This research offers insightful information about the model's ability to generalise in realistic situations. ©2023 ieee.

关键词： Image annotation

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Indoor Lighting Estimation using an Event Camera

Indoor Lighting Estimation using an Event Camera

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

作者： Chen, Zehao Zheng, Qian Niu, Peisong Tang, Huajin Pan, Gang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781665445092

Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed problem under this setup, based on which an optimization-based method and a learning-based method are proposed. Our experimental results validate that our approaches not only achieve superior performance for indoor lighting estimation (especially for the close light) but also significantly alleviate the intensity-distance ambiguity.

关键词： Learning systems computer vision Lighting Estimation Dynamic range Cameras Turning

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Seeing in Extra Darkness Using a Deep-Red Flash

Seeing in Extra Darkness Using a Deep-Red Flash

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

作者： Xiong, Jinhui Wang, Jian Heidrich, Wolfgang Nayar, Shree KAUST Daejeon South Korea Snap Res Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620 nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.

关键词： Training Photography Sensitivity Modulation Prototypes Cameras pattern recognition

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