2016 IEEE Winter Applications of Computer Vision Workshops (WACVW)最新文献

Detecting plains and Grevy's Zebras in the realworld 在现实世界中探测平原和格雷维斑马

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470122

J. Parham, C. Stewart

引用次数: 12

Unsupervised underwater fish detection fusing flow and objectiveness 融合流性和客观性的无监督水下鱼类检测

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470121

David C. Zhang, Giorgos Kopanas, C. Desai, S. Chai, M. Piacentino

引用次数: 20

Low resolution vehicle re-identification based on appearance features for wide area motion imagery 基于广域运动图像外观特征的低分辨率车辆再识别

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470114

Mickael Cormier, L. Sommer, Michael Teutsch

引用次数: 24

Image Surveillance Assistant 影像监控助理

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470119

Michael Maynord, S. Bhattacharya, D. Aha

引用次数: 4

Scene-independent feature- and classifier-based vehicle headlight and shadow removal in video sequences 视频序列中基于场景无关特征和分类器的车辆前照灯和阴影去除

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470115

Qun Li, Edgar A. Bernal, Matthew Shreve, R. Loce

{"title":"Scene-independent feature- and classifier-based vehicle headlight and shadow removal in video sequences","authors":"Qun Li, Edgar A. Bernal, Matthew Shreve, R. Loce","doi":"10.1109/WACVW.2016.7470115","DOIUrl":"https://doi.org/10.1109/WACVW.2016.7470115","url":null,"abstract":"Detection of moving and foreground objects is a key step in video-based object tracking within computer vision applications such as surveillance and traffic monitoring. Foreground object detection and segmentation is usually performed based on appearance. Hence, significant detection errors can be incurred due to shadows and light sources. Most existing shadow detection algorithms exploit a large set of assumptions to limit complexity, and at the same time, rely on carefully selected parameters either in the shadow model or the decision threshold. This limits their accuracy and extensibility to different scenarios. Furthermore, most traditional shadow detection algorithms operate on each pixel in the originally detected foreground mask and make pixel-wise decisions, which is not only time-consuming but also error-prone. Little work has been done to address false foreground detection caused by vehicle headlights during nighttime. In this paper, we introduce an efficient and effective algorithm for headlight/shadow removal in modelbased foreground detection via background estimation and subtraction. The underlying assumption is that headlights and shadows do not significantly affect the texture of the background. We train a classifier to discriminate between background affected and unaffected by shadows or headlights in a novel intermediate feature space. Advantages resulting from the choice of feature space in our approach include robustness to differences in background texture (i.e., the method is not scene-dependent), larger discriminability between positive and negative samples, and simplification of the training process.","PeriodicalId":185674,"journal":{"name":"2016 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131208","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: 5

Model-less and model-based computationally efficient motion estimation for video compression in transportation applications 无模型和基于模型的高效运动估计在交通视频压缩中的应用

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470120

Edgar A. Bernal, Qun Li, Orhan Bulan, Wencheng Wu, S. Schweid

{"title":"Model-less and model-based computationally efficient motion estimation for video compression in transportation applications","authors":"Edgar A. Bernal, Qun Li, Orhan Bulan, Wencheng Wu, S. Schweid","doi":"10.1109/WACVW.2016.7470120","DOIUrl":"https://doi.org/10.1109/WACVW.2016.7470120","url":null,"abstract":"Block-based motion estimation is an important component in many video coding standards that aims at removing temporal redundancy between neighboring frames. Traditional methods for block-based motion estimation such as the Exhaustive Block Matching Algorithm (EBMA) are capable of achieving good matching performance but are computationally expensive. Alternatives to EBMA have been proposed to reduce the amount of search points by trading off matching optimality with computational resources. Although they exploit shared local spatial attributes around the target block, they fail to take advantage of the characteristics of the video sequences acquired with stationary cameras used in transportation and surveillance applications, where motion patterns are largely regularized; often, they also fail to yield semantically meaningful motion vector fields. In this paper, we propose two alternative approaches to improve the efficiency of motion estimation in video compression: (i) a highly efficient model-less approach that estimates the direction and magnitude of motion of objects in the scene and predicts the optimal search direction/neighborhood location for motion vectors; and (ii) a model-based approach that learns the dominant spatiotemporal characteristics of the motion patterns captured in the video via statistical models and enables reduced searches according to the constructed models. We demonstrate via experimental validation that the proposed methods attain computational savings, achieve improved reconstruction error and prediction capabilities for a given search neighborhood size, and yield more semantically meaningful motion vector fields when coupled with traditional motion estimation algorithms.","PeriodicalId":185674,"journal":{"name":"2016 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127237947","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: 1

Filling in the blanks: reconstructing microscopic crowd motion from multiple disparate noisy sensors 填补空白:从多个不同的噪声传感器重建微观人群运动

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470118

Sejong Yoon, Mubbasir Kapadia, Pritish Sahu, V. Pavlovic

引用次数: 7

Superpixels shape analysis for carried object detection 用于携带目标检测的超像素形状分析

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470116

Blanca Delgado, Khalid Tahboub, E. Delp

引用次数: 4

Efficient object annotation for surveillance and automotive applications 用于监视和汽车应用的高效对象注释

2016 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2016-03-10 DOI: 10.1109/WACVW.2016.7470117

S. Swetha, Anand Mishra, Guruprasad M. Hegde, C. V. Jawahar

引用次数: 1