CPS: TTP Option: Medium: Discovering and Resolving Anomalies in Smart Cities

CPS：TTP 选项：中：发现并解决智慧城市中的异常情况

基本信息

批准号：
2038612
负责人：
Srinivasa Narasimhan
金额：
$ 120万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038612&HistoricalAwards=false
关键词：
CPS TTP Option Medium Discovering

项目摘要

Understanding complex activity due to humans and vehicles in a large environment like a city neighborhood or even an entire city is one of the main goals of smart cities. The activities are heterogeneous, distributed, vary over time and mutually interact in many ways, making them hard to capture and understand and mitigate issues in a timely manner. While there has been tremendous progress in capturing aggregate statistics that helps in traffic and city management as well as personal planning and scheduling, much of this work ignores anomalous patterns. Examples include protests, erratic driving, near accidents, construction zone activity, and numerous others. Discovering and resolving anomalies is challenging for many reasons as they are complex and rare, depend on the context and depend on the spatial and temporal extent over which they are observed. There are potentially a large number of anomalies or anomalous patterns, so they are impossible to label and describe manually. The PIs will conduct research to address automatic discovery and resolution of anomalous patterns in smart city visual data. The PIs will leverage the large amount of visual data they have access to ranging from cameras at many intersections in Pittsburgh, around the Carnegie Mellon University neighborhood, cameras installed on public buses, and physical distribution networks in the city. The project will include the following four closely integrated research thrusts: (1) Extracting anomalies in the presence of noise due to visual processing algorithms, (2) Automatically discover anomalies at different spatial and temporal scales with intelligent coordinated and distributed planning, (3) discovering the relationship with anomalies and context, and (4) Resolving Anomalies through Hard and Soft Actuation using both automatic and human-in-the-loop methods. The work will enable the following applications: Safer and more efficient roads, monitoring the roadway infrastructure and roadside, maximizing the distribution services and informing decisions on health policy (including COVID-19). The project will be conducted in collaboration with several stakeholders - multiple infrastructure and traffic management startups and local city government - in a comprehensive transition to practice program designed to deploy the research in the real world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解城市社区甚至整个城市等大环境中人类和车辆的复杂活动是智慧城市的主要目标之一。这些活动是异构的、分布式的、随时间变化的，并且以多种方式相互交互，使得它们难以及时捕获、理解和缓解问题。虽然在获取有助于交通和城市管理以及个人规划和日程安排的总体统计数据方面取得了巨大进步，但这项工作大部分都忽略了异常模式。例子包括抗议、不稳定驾驶、险些发生事故、建筑区活动等等。由于多种原因，发现和解决异常具有挑战性，因为它们复杂且罕见，取决于上下文以及观察到的空间和时间范围。可能存在大量异常或异常模式，因此无法手动标记和描述。 PI 将开展研究，以解决智慧城市视觉数据中异常模式的自动发现和解决问题。 PI 将利用他们可以获得的大量视觉数据，包括匹兹堡许多十字路口、卡内基梅隆大学附近的摄像头、公共巴士上安装的摄像头以及城市的物流网络。该项目将包括以下四个紧密结合的研究重点：（1）通过视觉处理算法在存在噪声的情况下提取异常，（2）通过智能协调和分布式规划自动发现不同空间和时间尺度的异常，（3）发现异常和上下文的关系，以及 (4) 使用自动和人机交互方法通过硬驱动和软驱动解决异常。这项工作将实现以下应用：更安全、更高效的道路、监控道路基础设施和路边、最大限度地提高配送服务并为卫生政策决策（包括 COVID-19）提供信息。该项目将与多个利益相关者（多个基础设施和交通管理初创公司以及当地市政府）合作进行，全面过渡到旨在在现实世界中部署研究的实践计划。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Traffic4D: Single View Longitudinal 4D Reconstruction of Repetitious Activity using Self-Supervised Experts

Traffic4D：使用自我监督专家对重复活动进行单视图纵向 4D 重建

DOI：
发表时间：
2021-07
期刊：
IEEE Intelligent Vehicles Symposium
影响因子：
0
作者：
Li, Fangyu;Reddy, N. Dinesh;Chen, Xudong;Narasimhan, Srinivasa G.
通讯作者：
Narasimhan, Srinivasa G.

WALT: Watch And Learn 2D amodal representation from Time-lapse imagery

WALT：观看并学习延时图像的 2D 非模态表示

DOI：
10.1109/cvpr52688.2022.00914
发表时间：
2022-06-01
期刊：
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Dinesh Reddy Narapureddy;R. Tamburo;S. Narasimhan
通讯作者：
S. Narasimhan

Incorporating Queue Dynamics into Schedule-Driven Traffic Control

将队列动态纳入调度驱动的流量控制

DOI：
发表时间：
2021-08
期刊：
International Joint Conference on Artificial Intelligence
影响因子：
0
作者：
Hu, Hsu;Hawkes, Allen;Smith, Stephen F.
通讯作者：
Smith, Stephen F.

TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking

TesseTrack：端到端可学习多人关节式 3D 姿势跟踪

DOI：
发表时间：
2021-06
期刊：
International Conference on Computer Vision and Pattern Recognition (CVPR
影响因子：
0
作者：
Reddy, N Dinesh;Guigues, Laurent;Pischulini, Leonid;Eledath, Jayan;Narasimhan, Srinivasa
通讯作者：
Narasimhan, Srinivasa

Exploiting & Refining Depth Distributions with Triangulation Light Curtains

剥削

DOI：
发表时间：
2021-06
期刊：
IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
0
作者：
Yaadhav, Raaj;Ancha, Siddharth;Tamburo, Robert;Held, David;Narasimhan, Srinivasa G.
通讯作者：
Narasimhan, Srinivasa G.