2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras最新文献_第3页

Multiview social behavior analysis in work environments 工作环境中的多视角社会行为分析

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042910

Chih-Wei Chen, H. Aghajan

引用次数: 15

Demo: An embedded vision system for high frame rate visual servoing 演示:用于高帧率视觉伺服的嵌入式视觉系统

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042946

Zhenyu Ye, Yifan He, R. Pieters, B. Mesman, H. Corporaal, P. Jonker

引用次数: 10

PhD forum: Adaptive storage management in highly heterogeneous Smart Sensor systems 博士论坛:高度异构智能传感器系统中的自适应存储管理

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042949

Carsten Grenz, J. Hähner

引用次数: 4

Demo: Real-time indoors people tracking in scalable camera networks 演示:在可扩展的摄像机网络中实时跟踪室内人员

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042957

Vedran Jelaca, Sebastian Gruenwedel, Jorge Oswaldo Niño Castañeda, P. V. Hese, Dimitri Van Cauwelaert, P. Veelaert, W. Philips

引用次数: 7

PhD forum: A data mining approach for human activity learning in a multi-modal sensor system 博士论坛:多模态传感器系统中人类活动学习的数据挖掘方法

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042953

F. Vandewiele, C. Motamed

引用次数: 1

On-line control of active camera networks for computer vision tasks 用于计算机视觉任务的有源摄像机网络的在线控制

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1145/2530283

A. Ilie, G. Welch

{"title":"On-line control of active camera networks for computer vision tasks","authors":"A. Ilie, G. Welch","doi":"10.1145/2530283","DOIUrl":"https://doi.org/10.1145/2530283","url":null,"abstract":"Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for on-line camera control. We introduce a new automated camera control approach that consists of a stochastic performance metric and a constrained optimization method. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of the target dynamics and camera measurements. It can account for static and dynamic occlusions, accommodate requirements specific to the algorithm used to process the images, and incorporate other factors that can affect its results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithm. The approach can be applied to conventional surveillance tasks (e.g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e.g., markerless motion capture or 3D reconstruction).","PeriodicalId":385052,"journal":{"name":"2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134119618","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

Predictive duty cycle adaptation for wireless camera networks 无线摄像机网络的预测占空比自适应

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042908

Paul J. Shin, Henry Medeiros, Johnny Park, A. Kak

引用次数: 3

ENRA: Event-based network reconfiguration algorithm for Active Camera Networks ENRA:基于事件的有源摄像机网络重构算法

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042936

Michael Wittke, Alvaro del Amo Jimenez, Sascha Radike, Carsten Grenz, J. Hähner

{"title":"ENRA: Event-based network reconfiguration algorithm for Active Camera Networks","authors":"Michael Wittke, Alvaro del Amo Jimenez, Sascha Radike, Carsten Grenz, J. Hähner","doi":"10.1109/ICDSC.2011.6042936","DOIUrl":"https://doi.org/10.1109/ICDSC.2011.6042936","url":null,"abstract":"Smart cameras play an important role for security systems as they combine video sensing, video processing and communication within a single device. One weak point is that smart cameras are usually stationary and so occlusions may create blind spots in the system. These blind spots may be overcome by enhancing smart cameras with mobility (e.g., mounting them on unmanned vehicles) in order to create so called Active Cameras (ACs) and network them into AC networks. Nevertheless, mobility of cameras comes along with challenges in terms of coordination and configuration. Additionally, the per unit cost of AC networks is higher in comparison to static camera networks but the number of cameras needed to observe large areas, e.g. an apron of an airport, can be reduced considerably. In this paper, we present an algorithm paving the way for self-adaptation in AC networks. Based on the number of events occurring in the workspace ACs increase or decrease so-called spatial redundancy regions with neighboring nodes to balance the network's load. Simulation results show that the presented protocol increases the overall system's performance and is well-suited for dynamic scenarios.","PeriodicalId":385052,"journal":{"name":"2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114789931","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: 0

View-invariant 3D human body pose reconstruction using a monocular video camera 基于单目摄像机的三维人体姿态重建

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042900

Shian-Ru Ke, Jenq-Neng Hwang, Kung-Ming Lan, Shen-Zheng Wang

引用次数: 16

Target container: A target-centric parallel programming abstraction for video-based surveillance 目标容器:用于基于视频的监视的以目标为中心的并行编程抽象

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras Pub Date : 2011-10-13 DOI: 10.1109/ICDSC.2011.6042914

Kirak Hong, Stephen Smaldone, JunSuk Shin, D. Lillethun, L. Iftode, U. Ramachandran

{"title":"Target container: A target-centric parallel programming abstraction for video-based surveillance","authors":"Kirak Hong, Stephen Smaldone, JunSuk Shin, D. Lillethun, L. Iftode, U. Ramachandran","doi":"10.1109/ICDSC.2011.6042914","DOIUrl":"https://doi.org/10.1109/ICDSC.2011.6042914","url":null,"abstract":"We introduce a novel abstraction, the target container (TC), which serves as a parallel programming model and execution framework for developing complex applications for tracking multiple targets in a large-scale camera network. The key insight is to allow the domain expert (e.g., a vision researcher) to focus on the algorithmic details of target tracking and let the system deal with providing the computational resources (cameras, networking, and processing) to enable target tracking. Each TC has a one-to-one correspondence with a target, possibly tracked from multiple cameras. The domain expert provides the code modules for target tracking (such as detectors and trackers) as handlers to the TC system. The handlers are invoked dynamically by the TC system to discover new targets (detector) and to follow existing targets (tracker). The TC system also provides an interface for merging TCs whenever they are determined to be corresponding to the same target. This paper presents the design of the TC system, details of an experimental prototype, and an example application to demonstrate the simplicity of using the TC programming model.","PeriodicalId":385052,"journal":{"name":"2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129711179","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