Proceedings of the 13th International Conference on Distributed Smart Cameras最新文献_第2页

From PC2BIM: Automatic Model generation from Indoor Point Cloud 来自PC2BIM:室内点云自动模型生成

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349825

Danielle Tchuinkou Kwadjo, Erman Nghonda Tchinda, C. Bobda, Nareph Menadjou, Cedrique Fotsing, Nafissetou Nziengam, Nafissetou Nziengam

引用次数: 6

Automatic Extrinsic Calibration of Camera Networks Based on Pedestrians 基于行人的摄像机网络外部自动标定

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349802

A. Truong, W. Philips, Junzhi Guan, N. Deligiannis, L. Abrahamyan

{"title":"Automatic Extrinsic Calibration of Camera Networks Based on Pedestrians","authors":"A. Truong, W. Philips, Junzhi Guan, N. Deligiannis, L. Abrahamyan","doi":"10.1145/3349801.3349802","DOIUrl":"https://doi.org/10.1145/3349801.3349802","url":null,"abstract":"Extrinsic camera calibration is essential for any computer vision tasks in a camera network. Usually, researchers place calibration objects in the scene to calibrate the cameras. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. It is based on the analysis of pedestrian tracks without other calibration objects. Compared to the state of the art, the new method is fully automatic and robust. Our method detects human poses in the camera images and then models walking persons as vertical sticks. We propose a brute-force method to determine the pedestrian correspondences in multiple camera images. This information along with 3D estimated locations of the head and feet of the pedestrians are then used to compute the camera extrinsic matrices. We verified the robustness of the method in different camera setups and for both single pedestrian and multiple walking people. The results show that the proposed method can obtain the triangulation error of a few centimeters. Typically, it requires 40 seconds of collecting data from walking people to reach this accuracy in controlled environments and a few minutes for uncontrolled environments. As well as compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion automatically. Our proposed method could perform well in various situations such as multi-person, occlusions, or even at real intersections on the street.","PeriodicalId":299138,"journal":{"name":"Proceedings of the 13th International Conference on Distributed Smart Cameras","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130274177","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

Dynamic Obstacle Detection in Traffic Environments 交通环境中的动态障碍物检测

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3357134

G. Erabati, Helder Araújo

引用次数: 1

Blockchain-grade privacy protection in surveillance systems 监控系统中的区块链级隐私保护

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3357132

Andrea Zanotto, N. Conci, A. Montresor

引用次数: 2

Drone patrolling with reinforcement learning 强化学习的无人机巡逻

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349805

C. Piciarelli, G. Foresti

引用次数: 12

An IoT Edge-Fog-Cloud Architecture for Vision Based Planogram Integrity 基于视觉的平面完整性物联网边缘-雾云架构

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349807

Rocco Pietrini, Davide Manco, M. Paolanti, V. Placidi, E. Frontoni, P. Zingaretti

引用次数: 1

A Distributed Approach to 3D Reconstruction in Marker Motion Capture Systems 标记运动捕捉系统中三维重建的分布式方法

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349818

A. Cenedese, Luca Varotto

引用次数: 0

PLFCN

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349819

Shuo Liu, Wenrui Ding, Hongguang Li, Chunlei Liu

引用次数: 1

EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors 基于边缘的卷积神经网络目标检测器的平衡精度和性能

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349809

George Plastiras, C. Kyrkou, T. Theocharides

{"title":"EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors","authors":"George Plastiras, C. Kyrkou, T. Theocharides","doi":"10.1145/3349801.3349809","DOIUrl":"https://doi.org/10.1145/3349801.3349809","url":null,"abstract":"Visual intelligence at the edge is becoming a growing necessity for low latency applications and situations where real-time decision is vital. Object detection, the first step in visual data analytics, has enjoyed significant improvements in terms of state-of-the-art accuracy due to the emergence of Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex paradigms intrude increasing computational demands and hence prevent their deployment on resource-constrained devices. In this work, we propose a hierarchical framework that enables to detect objects in high-resolution video frames, and maintain the accuracy of state-of-the-art CNN-based object detectors while outperforming existing works in terms of processing speed when targeting a low-power embedded processor using an intelligent data reduction mechanism. Moreover, a use-case for pedestrian detection from Unmanned-Areal-Vehicle (UAV) is presented showing the impact that the proposed approach has on sensitivity, average processing time and power consumption when is implemented on different platforms. Using the proposed selection process our framework manages to reduce the processed data by 100x leading to under 4W power consumption on different edge devices.","PeriodicalId":299138,"journal":{"name":"Proceedings of the 13th International Conference on Distributed Smart Cameras","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669967","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

Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations 具有真实退化的单图像超分辨率深度超分辨率网络

Proceedings of the 13th International Conference on Distributed Smart Cameras Pub Date : 2019-09-09 DOI: 10.1145/3349801.3349823

Rao Muhammad Umer, G. Foresti, C. Micheloni

引用次数: 9