Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges最新文献

The case for admission control of mobile cameras into the live video analytics pipeline 允许控制移动摄像机进入实时视频分析管道的案例

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480151

Francescomaria Faticanti, F. Bronzino, F. Pellegrini

引用次数: 0

Enabling high frame-rate UHD real-time communication with frame-skipping 通过跳帧实现高帧率UHD实时通信

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3481582

Tingfeng Wang, Zili Meng, Mingwei Xu, Rui Han, Honghao Liu

引用次数: 2

Characterizing real-time dense point cloud capture and streaming on mobile devices 描述移动设备上的实时密集点云捕获和流

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480155

Jinhan Hu, Aashiq Shaikh, A. Bahremand, R. Likamwa

{"title":"Characterizing real-time dense point cloud capture and streaming on mobile devices","authors":"Jinhan Hu, Aashiq Shaikh, A. Bahremand, R. Likamwa","doi":"10.1145/3477083.3480155","DOIUrl":"https://doi.org/10.1145/3477083.3480155","url":null,"abstract":"Point clouds are a dense compilation of millions of points that can advance content creation and interaction in various emerging applications such as Augmented Reality (AR). However, point clouds consist of per-point real-world spatial and color information that are too computationally intensive to meet real-time specifications, especially on mobile devices. To stream dense point cloud (PtCl) to mobile devices, existing solutions encode pre-captured point clouds, yet with PtCl capturing treated as a separate offline operation. To discover more insights, we combine PtCl capturing and streaming as an entire pipeline and build a research prototype to study the bottlenecks of its real-time usage on mobile devices, consisting of a depth sensor with high precision and resolution, an edge-computing development board, and a smartphone. In a custom Unity app, we monitor the latency of each operation from the capturing to the rendering, as well as the energy efficiency of the board and the smartphone working at different point cloud resolutions. Results reveal that a toolset helping users efficiently capture, stream, and process color and depth data is the key enabler to real-time PtCl capturing and streaming on mobile devices.","PeriodicalId":206784,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128315836","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

Cost effective processing of detection-driven video analytics at the edge 具有成本效益的边缘检测驱动视频分析处理

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480156

Md. Adnan Arefeen, M. Y. S. Uddin

引用次数: 0

Towards memory-efficient inference in edge video analytics 在边缘视频分析中实现高效内存推理

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480150

Arthi Padmanabhan, A. Iyer, G. Ananthanarayanan, Yuanchao Shu, Nikolaos Karianakis, G. Xu, R. Netravali

引用次数: 5

Decentralized modular architecture for live video analytics at the edge 分散的模块化架构，用于边缘的实时视频分析

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480153

Sri Pramodh Rachuri, F. Bronzino, Shubham Jain

{"title":"Decentralized modular architecture for live video analytics at the edge","authors":"Sri Pramodh Rachuri, F. Bronzino, Shubham Jain","doi":"10.1145/3477083.3480153","DOIUrl":"https://doi.org/10.1145/3477083.3480153","url":null,"abstract":"Live video analytics have become a key technology to support surveillance, security, traffic control, and even consumer multimedia applications in real time. The continuous growth in number of networked video cameras will further increase their widespread adoption. Yet, until now, developments in video analytics have largely focused on using fixed cameras, omitting the ever-growing presence of mobile cameras such as car dash-cams, drones, and smartphones. Edge computing, coupled with centralized clouds, has helped alleviate the network traffic and processing load, reducing latency and data transmissions. However, the current approach of processing video feeds through a hierarchy of clusters across a somewhat predictable path in the network will not be sufficient to support the integration of mobile feeds into the video analytics architecture. In this paper, we argue that a crucial step towards supporting heterogeneous camera sources is the adoption of a flat edge computing architecture. Such architecture should enable the dynamic distribution of processing loads through distributed computing points of presence, rapidly adapting to sudden changes in traffic conditions. In support of this hypothesis, we present exploratory results that show that smartly distributing and processing vision modules in parallel across available edge compute nodes can ultimately lead to better resource utilization and improved performance.","PeriodicalId":206784,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126540684","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

Auto-SDA: Automated video-based social distancing analyzer Auto-SDA:基于视频的自动社交距离分析仪

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI: 10.1145/3477083.3480154

Mahshid Ghasemi, Z. Kostić, Javad Ghaderi, G. Zussman

{"title":"Auto-SDA: Automated video-based social distancing analyzer","authors":"Mahshid Ghasemi, Z. Kostić, Javad Ghaderi, G. Zussman","doi":"10.1145/3477083.3480154","DOIUrl":"https://doi.org/10.1145/3477083.3480154","url":null,"abstract":"Social distancing can reduce infection rates in respiratory pandemics such as COVID-19, especially in dense urban areas. To assess pedestrians' compliance with social distancing policies, we use the pilot site of the PAWR COSMOS wireless edge-cloud testbed in New York City to design and evaluate an Automated video-based Social Distancing Analyzer (Auto-SDA) pipeline. Auto-SDA derives pedestrians' trajectories and measures the duration of close proximity events. It relies on an object detector and a tracker, however, to achieve highly accurate social distancing analysis, we design and incorporate 3 modules into Auto-SDA: (i) a calibration module that converts 2D pixel distances to 3D on-ground distances with less than 10 cm error, (ii) a correction module that identifies pedestrians who were missed or assigned duplicate IDs by the object detection-tracker and rectifies their IDs, and (iii) a group detection module that identifies affiliated pedestrians (i.e., pedestrians who walk together as a social group) and excludes them from the social distancing violation analysis. We applied Auto-SDA to videos recorded at the COSMOS pilot site before the pandemic, soon after the lockdown, and after the vaccines became broadly available, and analyzed the impacts of the social distancing protocols on pedestrians' behaviors and their evolution. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians violated the social distancing protocols, whereas this percentage increased to 65% after the vaccines became available. Moreover, after the lockdown, 0-20% of the pedestrians were affiliated with a social group, compared to 10-45% once the vaccines became available. Finally, following the lockdown, the density of the pedestrians at the intersection decreased by almost 50%.","PeriodicalId":206784,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987045","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: 14