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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016"

共 25 条记录，以下是1-10 订阅

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

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

作者： Lap Quoc Tran Huan Duc Vi Asilla Tokyo Japan

ISBN: (纸本)9798350365474

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. the integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lap Quoc Tran Huan Duc Vi Asilla

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

关键词： computer vision Accuracy Urban areas Cameras Real-time systems Trajectory Spatiotemporal phenomena

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Report on the 29th ieee conference on computer vision and pattern recognition (CVPR 2016)

Report on the 29th IEEE conference on computer vision and pa...

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作者： Uranishi, Yuki Cybermedia Center Osaka University Osaka Japan

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Deep Residual Network with Enhanced Upscaling Module for Super-Resolution 31

Deep Residual Network with Enhanced Upscaling Module for Sup...

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

作者： Kim, Jun-Hyuk Lee, Jong-Seok Yonsei Univ Sch Integrated Technol Seoul South Korea

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Single image super-resolution (SR) have recently shown great performance thanks to the advances in deep learning. In the middle of the deep networks for SR, a part that increases image resolution is required, for which a sub-pixel convolution layer is known as an efficient way. However, we argue that the method has room for improvement, and propose an enhanced upscaling module (EUM), which achieves improvement by utilizing nonlinear operations and skip connections. Employing our proposed EUM, we propose a novel deep residual network for SR, called EUSR. Our proposed EUSR was ranked in the 9th place among 24 teams in terms of SSIM in track 1 of the NTIRE 2018 SR Challenge [25]. In addition, we experimentally show that EUSR has comparable performance on x2 and x4 SR for four benchmark datasets to the state-of-the-art methods, and outperforms them on a large scaling factor (x8).

关键词： Convolution Image resolution Feature extraction Training Image reconstruction computer vision Signal resolution

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ieee computer Society conference on computer vision and pattern recognition workshops

IEEE Computer Society Conference on Computer Vision and Patt...

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29th ieee conference on computer vision and pattern recognition workshops, cvprw 2016

ISBN: (纸本)9781467388504

the proceedings contain 192 papers. the topics discussed include: feature vector compression based on least error quantization;a comprehensive analysis of deep learning based representation for face recognition;two-stream CNNs for gesture-based verification and identification: learning user style;CALIPER: continuous authentication layered with integrated PKI encoding recognition;latent fingerprint image segmentation using fractal dimension features and weighted extreme learning machine ensemble;a comparison of human and automated face verification accuracy on unconstrained image sets;offline signature verification based on bag-of-VisualWords model using KAZE features and weighting schemes;implementation of fixed-length template protection based on homomorphic encryption with application to signature biometrics;seeing the forest from the trees: a holistic approach to near-infrared heterogeneous face recognition;a novel visualization tool for evaluating the accuracy of 3d sensing and reconstruction algorithms for automatic dormant pruning applications;a pointing gesture based egocentric interaction system: dataset, approach and application;multimodal multi-stream deep learning for egocentric activity recognition;sparse kernel machines for discontinuous registration and nonstationary regularization;and accurate small deformation exponential approximant to integrate large velocity fields: application to image registration.

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Proceedings of the ieee computer Society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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29th ieee conference on computer vision and pattern recognition, CVPR 2016

ISBN: (纸本)9781467388504

the proceedings contain 640 papers. the topics discussed include: generation and comprehension of unambiguous object description;image question answering using convolutional neural network with dynamic parameter prediction;neural module networks;learning to assign orientations to feature points;affinity CNN: learning pixel-centric pairwise relations for figure/ground embedding;occlusion boundary detection via deep exploration of context;exploit bounding box annotations for multi-label object recognition;MCMC shape sampling for image segmentation with nonparametric shape priors;learning action maps of large environments via first-person vision;sample and filter: nonparametric scene parsing via efficient filtering;training region-based object detectors with online hard example mining;learning with side information through modality hallucination;HyperNet: towards accurate region proposal generation and joint object detection;macroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flight;ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels;hierarchical recurrent neural encoder for video representation with application to captioning;and from keyframes to key objects: video summarization by representative object proposal selection.

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PETS 2016: Dataset and Challenge 29

PETS 2016: Dataset and Challenge

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29th ieee conference on computer vision and pattern recognition workshops (cvprw)

作者： Patino, Luis Cane, Tom Vallee, Alain Ferryman, James Univ Reading Computat Vis Grp Reading RG6 6AY Berks England BMT Grp Ltd Teddington TW11 8LZ Middx England SAGEM F-92659 Boulogne France

ISBN: (纸本)9781509014378

this paper describes the datasets and computer vision challenges that form part of the PETS 2016 workshop. PETS 2016 addresses the application of on-board multi sensor surveillance for protection of mobile critical assets. the sensors (visible and thermal cameras) are mounted on the asset itself and surveillance is performed around the asset. Two datasets are provided: (1) a multi sensor dataset as used for the PETS2014 challenge which addresses protection of trucks (the ARENA Dataset);and (2) a new dataset - the IPATCH Dataset - addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. the dataset specifically addresses several vision challenges set in the PETS 2016 workshop, and corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis ('simple' event detection: the behaviour recognition of a single actor) and High-Level Video Analysis ('complex' event detection: the behaviour and interaction recognition of several actors).

关键词： Monitoring

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A Scalable High-Performance Hardware Architecture for Real-Time Stereo vision by Semi-Global Matching 29

A Scalable High-Performance Hardware Architecture for Real-T...

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29th ieee conference on computer vision and pattern recognition (CVPR)

作者： Hofmann, Jaco Korinth, Jens Koch, Andreas Tech Univ Darmstadt Embedded Syst & Applicat Grp ESA Darmstadt Germany

ISBN: (纸本)9781509014378

Perceiving distance from two camera images, a task called stereo vision, is fundamental for many applications in robotics or automation. However, algorithms that compute this information at high accuracy have a high computational complexity. One such algorithm, Semi Global Matching (SGM), performs well in many stereo vision benchmarks, while maintaining a manageable computational complexity. Nevertheless, CPU and GPU implementations of this algorithm often fail to achieve real-time processing of camera images, especially in power-constrained embedded environments. this work presents a novel architecture to calculate disparities through SGM. the proposed architecture is highly scalable and applicable for low-power embedded as well as high-performance multi-camera high-resolution applications.

关键词： Stereo vision

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Embedded Computing Framework for vision-based Real-time Surround threat Analysis and Driver Assistance 29

Embedded Computing Framework for Vision-based Real-time Surr...

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29th ieee conference on computer vision and pattern recognition (CVPR)

作者： Lu, Frankie Lee, Sean Satzoda, Ravi Kumar Trivedi, Mohan Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781509014378

In this paper, we present a distributed embedded vision system that enables surround scene analysis and vehicle threat estimation. the proposed system analyzes the surroundings of the ego-vehicle using four cameras, each connected to a separate embedded processor. Each processor runs a set of optimized vision-based techniques to detect surrounding vehicles, so that the entire system operates at real-time speeds. this setup has been demonstrated on multiple vehicle testbeds with high levels of robustness under real-world driving conditions and is scalable to additional cameras. Finally, we present a detailed evaluation which shows over 95% accuracy and operation at nearly 15 frames per second.

关键词： Vehicles

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Kernel ELM and CNN based Facial Age Estimation 29

Kernel ELM and CNN based Facial Age Estimation

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29th ieee conference on computer vision and pattern recognition (CVPR)

作者： Gurpinar, Furkan Kaya, Heysem Dibeklioglu, Hamdi Salah, Albert Ali Bogazici Univ Program Computat Sci & Engn Istanbul Turkey Namik Kemal Univ Dept Comp Engn Tekirdag Turkey Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

e propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pre-trained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

关键词： Learning systems

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