2019 IEEE Winter Conference on Applications of Computer Vision (WACV)最新文献_第2页

Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance 面向广域、远程监视的移动UGV多模态检测融合

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00207

Matt Brown, Keith Fieldhouse, E. Swears, Paul Tunison, Adam Romlein, A. Hoogs

{"title":"Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance","authors":"Matt Brown, Keith Fieldhouse, E. Swears, Paul Tunison, Adam Romlein, A. Hoogs","doi":"10.1109/WACV.2019.00207","DOIUrl":"https://doi.org/10.1109/WACV.2019.00207","url":null,"abstract":"We introduce a self-contained, mobile surveillance system designed to remotely detect and track people in real time, at long ranges, and over a wide field of view in cluttered urban and natural settings. The system is integrated with an unmanned ground vehicle, which hosts an array of four IR and four high-resolution RGB cameras, navigational sensors, and onboard processing computers. High-confidence, low-false-alarm-rate person tracks are produced by fusing motion detections and single-frame CNN person detections between co-registered RGB and IR video streams. Processing speeds are increased by using semantic scene segmentation and a tiered inference scheme to focus processing on the most salient regions of the 43° x 7.8° composite field of view. The system autonomously produces alerts of human presence and movement within the field of view, which are disseminated over a radio network and remotely viewed on a tablet computer. We present an ablation study quantifying the benefits that multi-sensor, multi-detector fusion brings to the problem of detecting people in challenging outdoor environments with shadows, occlusions, clutter, and variable weather conditions.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125732000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Exploring Classification of Histological Disease Biomarkers From Renal Biopsy Images 从肾活检图像中探索组织学疾病生物标志物的分类

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00016

Puneet Mathur, Meghna P. Ayyar, R. Shah, S. Sharma

{"title":"Exploring Classification of Histological Disease Biomarkers From Renal Biopsy Images","authors":"Puneet Mathur, Meghna P. Ayyar, R. Shah, S. Sharma","doi":"10.1109/WACV.2019.00016","DOIUrl":"https://doi.org/10.1109/WACV.2019.00016","url":null,"abstract":"Identification of diseased kidney glomeruli and fibrotic regions remains subjective and time-consuming due to complete dependence on an expert kidney pathologist. In an attempt to automate the classification of glomeruli into normal and abnormal morphology and classification of fibrosis patches into mild, moderate and severe categories, we investigate three deep learning techniques: traditional transfer learning, pre-trained deep neural networks for feature extraction followed by supervised classification, and a novel Multi-Gaze Attention Network (MGANet) that uses multi-headed self-attention through parallel residual skip connections in a CNN architecture. Emperically, while the transfer learning models such as ResNet50, InceptionResNetV2, VGG19 and InceptionV3 acutely under-perform in the classification tasks, the Logistic Regression model augmented with features extracted from the InceptionResNetV2 shows promising results. Additionally, the experiments effectively ascertain that the proposed MGANet architecture outperforms both the former baseline techniques to establish the state of the art accuracy of 87.25% and 81.47% for glomerluli and fibrosis classification, respectively on the Renal Glomeruli Fibrosis Histopathological (RGFH) database.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125953356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos 天空视频中太阳辐照度预测的深度学习方法

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00234

Talha Ahmad Siddiqui, Samarth Bharadwaj, S. Kalyanaraman

{"title":"A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos","authors":"Talha Ahmad Siddiqui, Samarth Bharadwaj, S. Kalyanaraman","doi":"10.1109/WACV.2019.00234","DOIUrl":"https://doi.org/10.1109/WACV.2019.00234","url":null,"abstract":"Ahead-of-time forecasting of incident solar-irradiance on a panel is indicative of expected energy yield and is essential for efficient grid distribution and planning. Traditionally, these forecasts are based on meteorological physics models whose parameters are tuned by coarse-grained radiometric tiles sensed from geo-satellites. This research presents a novel application of deep neural network approach to observe and estimate short-term weather effects from videos. Specifically, we use time-lapsed videos (sky-videos) obtained from upward facing wide-lensed cameras (sky-cameras) to directly estimate and forecast solar irradiance. We introduce and present results on two large publicly available datasets obtained from weather stations in two regions of North America using relatively inexpensive optical hardware. These datasets contain over a million images that span for 1 and 12 years respectively, the largest such collection to our knowledge. Compared to satellite based approaches, the proposed deep learning approach significantly reduces the normalized mean-absolute-percentage error for both nowcasting, i.e. prediction of the solar irradiance at the instance the frame is captured, as well as forecasting, ahead-of-time irradiance prediction for a duration for upto 4 hours.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130046565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment 基于实例感知跟踪和动态模型刷新的在线多目标跟踪

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00023

Peng Chu, Heng Fan, C. C. Tan, Haibin Ling

{"title":"Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment","authors":"Peng Chu, Heng Fan, C. C. Tan, Haibin Ling","doi":"10.1109/WACV.2019.00023","DOIUrl":"https://doi.org/10.1109/WACV.2019.00023","url":null,"abstract":"Recent progresses in model-free single object tracking (SOT) algorithms have largely inspired applying SOT to multi-object tracking (MOT) to improve the robustness as well as relieving dependency on external detector. However, SOT algorithms are generally designed for distinguishing a target from its environment, and hence meet problems when a target is spatially mixed with similar objects as observed frequently in MOT. To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models. In particular, we construct each target model by fusing information for distinguishing target both from background and other instances (tracking targets). To conserve uniqueness of all target models, our instance-aware tracker considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy. Another contribution we make is a dynamic model refreshing strategy learned by a convolutional neural network. This strategy helps to eliminate initialization noise as well as to adapt to variation of target size and appearance. To show the effectiveness of the proposed approach, it is evaluated on the popular MOT15 and MOT16 challenge benchmarks. On both benchmarks, our approach achieves the best overall performances in comparison with published results.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132449987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 98

ThunderNet: A Turbo Unified Network for Real-Time Semantic Segmentation ThunderNet:一个用于实时语义分割的Turbo统一网络

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00195

Wei Xiang, Hongda Mao, V. Athitsos

引用次数: 27

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision 从更少的数据中学习:计算机视觉的统一数据子集选择和主动学习框架

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00142

Vishal Kaushal, Rishabh K. Iyer, S. Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan

{"title":"Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision","authors":"Vishal Kaushal, Rishabh K. Iyer, S. Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan","doi":"10.1109/WACV.2019.00142","DOIUrl":"https://doi.org/10.1109/WACV.2019.00142","url":null,"abstract":"Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry. Their data curation poses the challenges of expensive human labeling, inadequate computing resources and larger experiment turn around times. Training data subset selection and active learning techniques have been proposed as possible solutions to these challenges. A special class of subset selection functions naturally model notions of diversity, coverage and representation and can be used to eliminate redundancy thus lending themselves well for training data subset selection. They can also help improve the efficiency of active learning in further reducing human labeling efforts by selecting a subset of the examples obtained using the conventional uncertainty sampling based techniques. In this work, we empirically demonstrate the effectiveness of two diversity models, namely the Facility-Location and Dispersion models for training-data subset selection and reducing labeling effort. We demonstrate this across the board for a variety of computer vision tasks including Gender Recognition, Face Recognition, Scene Recognition, Object Detection and Object Recognition. Our results show that diversity based subset selection done in the right way can increase the accuracy by upto 5 - 10% over existing baselines, particularly in settings in which less training data is available. This allows the training of complex machine learning models like Convolutional Neural Networks with much less training data and labeling costs while incurring minimal performance loss.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121712239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 61

Human-Centric Light Sensing and Estimation From RGBD Images: The Invisible Light Switch 基于RGBD图像的以人为中心的光感知与估计:不可见的光开关

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00050

T. Tsesmelis, Irtiza Hasan, M. Cristani, A. D. Bue, Fabio Galasso

{"title":"Human-Centric Light Sensing and Estimation From RGBD Images: The Invisible Light Switch","authors":"T. Tsesmelis, Irtiza Hasan, M. Cristani, A. D. Bue, Fabio Galasso","doi":"10.1109/WACV.2019.00050","DOIUrl":"https://doi.org/10.1109/WACV.2019.00050","url":null,"abstract":"Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123876306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Video-Rate Video Inpainting 视频速率视频绘制

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00170

Rito Murase, Yan Zhang, Takayuki Okatani

引用次数: 4

Cascade Attention Machine for Occluded Landmark Detection in 2D X-Ray Angiography 二维x线血管造影中闭塞地标检测的级联注意机

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00017

Liheng Zhang, V. Singh, Guo-Jun Qi, Terrence Chen

{"title":"Cascade Attention Machine for Occluded Landmark Detection in 2D X-Ray Angiography","authors":"Liheng Zhang, V. Singh, Guo-Jun Qi, Terrence Chen","doi":"10.1109/WACV.2019.00017","DOIUrl":"https://doi.org/10.1109/WACV.2019.00017","url":null,"abstract":"In cardiac interventions, localization of guiding catheter tip in 2D fluoroscopic images is important to specify ves-sel branches and calibrate vessels with stenosis. While detection of guiding catheter tip is not trivial in contrast-free images due to low dose radiation as well as occlusion by other devices, it is even more challenging in contrast-filled images. As contrast-filled vessels become visible in X-ray imaging, the landmark of guiding catheter tip can often be completely occluded by the contrast medium. It is difficult even for human eyes to precisely localize the catheter tip from a single angiography image. Physicians have to rely on information before the inject of contrast medium to localize the guiding catheter tip occluded by contrast medium. Automatic landmark detection when occlusion happens is important and can significantly simplify the intervention workflow. To address this problem, we propose a novel Cascade Attention Machine (CAM) model. It borrows the idea of how human experts localize the catheter tip by first per-forming landmark detection when occlusion does not hap-pen, then leveraging this information as prior knowledge to assist the occluded detection. Attention maps are computed from non-occluded detection to further refine the heatmaps for occluded detection to guide the inference focusing on related regions. Experiments on X-ray angiography demonstrate the promising performance compared with the state-of-the-art baselines. It shows that the CAM can capture the relation between situations with and without occlusion to achieve precise detection of occluded landmark.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126964853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Coupled Generative Adversarial Network for Continuous Fine-Grained Action Segmentation 连续细粒度动作分割的耦合生成对抗网络

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00027

Harshala Gammulle, Tharindu Fernando, S. Denman, S. Sridharan, C. Fookes

{"title":"Coupled Generative Adversarial Network for Continuous Fine-Grained Action Segmentation","authors":"Harshala Gammulle, Tharindu Fernando, S. Denman, S. Sridharan, C. Fookes","doi":"10.1109/WACV.2019.00027","DOIUrl":"https://doi.org/10.1109/WACV.2019.00027","url":null,"abstract":"We propose a novel conditional GAN (cGAN) model for continuous fine-grained human action segmentation, that utilises multi-modal data and learned scene context information. The proposed approach utilises two GANs: termed Action GAN and Auxiliary GAN, where the Action GAN is trained to operate over the current RGB frame while the Auxiliary GAN utilises supplementary information such as depth or optical flow. The goal of both GANs is to generate similar 'action codes', a vector representation of the current action. To facilitate this process a context extractor that incorporates data and recent outputs from both modes is used to extract context information to aids recognition performance. The result is a recurrent GAN architecture which learns a task specific loss function from multiple feature modalities. Extensive evaluations on variants of the proposed model to show the importance of utilising different streams of information such as context and auxiliary information in the proposed network; and show that our model is capable of outperforming state-of-the-art methods for three widely used datasets: 50 Salads, MERL Shopping and Georgia Tech Egocentric Activities, comprising both static and dynamic camera settings.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114549403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18