US Ignite: Focus Area 1: Fast Autonomic Traffic Congestion Monitoring and Incident Detection through Advanced Networking, Edge Computing, and Video Analytics

US Ignite：重点领域 1：通过先进网络、边缘计算和视频分析进行快速自主交通拥堵监控和事件检测

• 批准号: 1647170
• 负责人: Abdallah Khreishah
• 金额: $ 60万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647170&HistoricalAwards=false
• 关键词: US; Ignite; Focus; Area; Fast

Video-based traffic monitoring systems have been widely used for traffic management, incident detection, intersection control, and public safety operations. Current designs pose critical challenges. First, it relies heavily on human operators to monitor and analyze video images. Second, commercially available computer vision technologies cannot satisfactorily handle severe conditions, such as weather and glare, which significantly impair video image quality. Third, the simultaneous transmission of numerous video signals to a central facility creates extreme demands on the communications network, which can lead to jamming. This project presents a novel approach that incorporates wireless sensor networks, hierarchical edge-computing, and advanced computer vision technology. The methods can be expanded to address a wide spectrum of potential applications including wrong-way driving alerts, congestion detection under bad weather conditions, accident scene management support, suspect vehicle tracking, wildfire detection and alert, and emergency evacuation, which could save lives and hundreds of billions of dollars annually. It also aligns with the smart city initiative.By using bluetooth/WiFi detection technology, the trajectories and speeds of vehicles equipped with such devices will be collected. This information, along with the captured video data, will be analyzed by the proposed computer vision software, installed at the edge of the network on cloudlets, to perform fast detection and prioritization of the video streams from different cameras. The proposed hierarchical edge-computing paradigm will not only enable real-time big data analysis at the edge but will also be demonstrated and actualized to perform timely efficient video analytics. Depending on the weather conditions, different detection and prioritization algorithms will be activated. Video coding will then be implemented to transmit the selected video streams to the central back-end system for further processing. If an incident is detected by the algorithm either at the edge or at the back-end, a necessary feedback action will be taken, such as calling an emergency group, the highway safety dispatch, or the police. Under a technical partnership with New Jersey Department of Transportation, multiple pilot tests of the proposed system will be implemented on selected highway corridors designated by the department.

基于视频的交通监控系统已广泛应用于交通管理、事件检测、交叉口控制和公共安全运营。当前的设计提出了严峻的挑战。首先，它严重依赖人工操作员来监控和分析视频图像。其次，商用计算机视觉技术无法令人满意地处理恶劣条件，例如天气和眩光，这会严重损害视频图像质量。第三，同时向中央设施传输大量视频信号对通信网络提出了极高的要求，这可能导致干扰。该项目提出了一种新颖的方法，融合了无线传感器网络、分层边缘计算和先进的计算机视觉技术。这些方法可以扩展到广泛的潜在应用，包括逆向驾驶警报、恶劣天气条件下的拥堵检测、事故现场管理支持、可疑车辆跟踪、野火检测和警报以及紧急疏散，这可以挽救生命和安全。每年数千亿美元。它还符合智慧城市倡议。通过使用蓝牙/WiFi检测技术，将收集配备此类设备的车辆的轨迹和速度。这些信息以及捕获的视频数据将由安装在云网络边缘的计算机视觉软件进行分析，以对来自不同摄像机的视频流进行快速检测和优先级排序。所提出的分层边缘计算范例不仅能够在边缘进行实时大数据分析，而且还将被演示和实现以执行及时高效的视频分析。根据天气条件，将激活不同的检测和优先级算法。然后将实施视频编码，将选定的视频流传输到中央后端系统进行进一步处理。如果算法在边缘或后端检测到事件，将采取必要的反馈行动，例如呼叫紧急小组、高速公路安全调度或警察。根据与新泽西州交通部的技术合作，拟议系统的多次试点测试将在该部门指定的选定高速公路走廊上进行。

项目成果

A New Online Approach for Moving Cast Shadow Suppression in Traffic Videos

交通视频中移动投射阴影抑制的新在线方法

• DOI: 10.1109/itsc48978.2021.9565049
• 发表时间: 2021-01
• 期刊: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC
• 作者: Ghahremannezhad, Hadi;Shi, Hang;Liu, Chengjun
• 通讯作者: Liu, Chengjun

Anomalous Driving Detection for Traffic Surveillance Video Analysis

用于交通监控视频分析的异常驾驶检测

• DOI: 10.1109/ist50367.2021.9651372
• 发表时间: 2021-01
• 期刊: 2021 IEEE International Conference on Imaging Systems and Techniques (IST
• 作者: Shi, Hang;Ghahremannezhad, Hadi;Liu, Chengjun
• 通讯作者: Liu, Chengjun

Towards Workload Balancing in Fog Computing Empowered IoT

在雾计算赋能的物联网中实现工作负载平衡

• DOI: 10.1109/tnse.2018.2852762
• 发表时间: 2020-01-01
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Q. Fan;N. Ansari
• 通讯作者: N. Ansari

A Statistical Modeling Method for Road Recognition in Traffic Video Analytics

交通视频分析中道路识别的统计建模方法

• DOI: 10.1109/coginfocom50765.2020.9237905
• 发表时间: 2020-09-23
• 期刊: 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)
• 作者: Hang Shi;Hadi Ghahremannezhad;Chengjun Liu
• 通讯作者: Chengjun Liu

Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks

车载网络中接收数据包的机会性处理在线算法

• DOI: 10.1109/tits.2018.2809917
• 发表时间: 2019-01-01
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Ala I. Al;Ammar Gharaibeh;Ihab Mohammed;Sayed Jahed Hussini;Abdallah Khreishah;Issa M. Khalil
• 通讯作者: Issa M. Khalil