SCC: Video Based Machine Learning for Smart Traffic Analysis and Management

SCC：基于视频的机器学习，用于智能流量分析和管理

• 批准号: 1922782
• 负责人: Sanjay Ranka
• 金额: $ 199.98万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922782&HistoricalAwards=false
• 关键词: SCC; Video; Based; Machine; Learning

The goal of this project is to further the ability of cities and communities to deploy technology that saves lives through safer transportation systems. The approach is to create open source analytics solutions to enable novel transportation applications that utilize data from low-cost video sensors. Video data are processed using edge computing (inexpensive computing hardware that performs analysis without storing significant amounts of data) in order to reduce the amount of data stored. Social dimensions of the research project emerge from the deep research partnership between the City and the University, with the goal to provide replicable and near-term social impacts. The project aligns with the Vision Zero concept to reduce traffic fatalities, with programs that are based on education, enforcement and design. By understanding the risk profile of an intersection through automated detection of near miss events, communities will be able to proactively design and alter streets and intersections to be safer. The goal of designing a smart city, when addressing the technical challenges at the intersection, street and system levels, has several research components. (i) Development of new algorithms for multi-target tracking: The problems of occlusion, temporal assignment of features to objects and target motion will be jointly formulated. (ii) Integrated optimization and simulation for signal control: We formulate the problem of estimating signal control parameters (offsets, phasing etc.) in a network as one of global optimization. (iii) Real-time reinforcement learning is a natural choice when online machine learning meets real world feedback from the City. Our ability to obtain and analyze continuous-time data at the network level will provide insights on how conflict points and patterns can change through the network. This is expected to impact decisions in traffic management, smart city planning and safety.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.

该项目的目标是提高城市和社区部署技术的能力，通过更安全的交通系统来拯救生命。该方法是创建开源分析解决方案，以实现利用低成本视频传感器数据的新型交通应用程序。使用边缘计算（无需存储大量数据即可执行分析的廉价计算硬件）处理视频数据，以减少存储的数据量。该研究项目的社会层面源于城市与大学之间的深入研究伙伴关系，其目标是提供可复制的近期社会影响。该项目与减少交通死亡人数的“零愿景”概念相一致，其计划基于教育、执行和设计。通过自动检测未遂事件来了解十字路口的风险状况，社区将能够主动设计和改造街道和十字路口，使其更加安全。在解决交叉路口、街道和系统层面的技术挑战时，设计智慧城市的目标包含多个研究组成部分。 (i) 开发多目标跟踪新算法：将联合制定遮挡、对象特征的时间分配和目标运动的问题。 (ii)信号控制的综合优化和仿真：我们将估计网络中的信号控制参数（偏移、定相等）的问题表述为全局优化之一。 (iii) 当在线机器学习满足城市的现实世界反馈时，实时强化学习是一个自然的选择。我们在网络级别获取和分析连续时间数据的能力将提供有关冲突点和模式如何通过网络发生变化的见解。预计这将影响交通管理、智慧城市规划和安全方面的决策。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

A Modern Intersection Data Analytics System for Pedestrian and Vehicular Safety

• DOI: 10.1109/itsc55140.2022.9921827
• 发表时间: 2022-10
• 期刊: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
• 作者: Tania Banerjee-Mishra;Ke Chen;Alejandro Almaraz;Rahul Sengupta;Yashaswi Karnati;Bryce Grame;E. Posadas;Subhadipto Poddar;R. Schenck;Jeremy Dilmore;Sivaramnakrishnan Srinivasan;A. Rangarajan;Sanjay Ranka

Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video

• DOI: 10.1145/3373647
• 发表时间: 2020-02-01
• 期刊: ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS
• 影响因子: 1.9
• 作者: Huang, Xiaohui;He, Pan;Ranka, Sanjay
• 通讯作者: Ranka, Sanjay

Learning Scene Dynamics from Point Cloud Sequences

• DOI: 10.1007/s11263-021-01551-y
• 发表时间: 2022-01-23
• 期刊: INTERNATIONAL JOURNAL OF COMPUTER VISION
• 影响因子: 19.5
• 作者: He, Pan;Emami, Patrick;Rangarajan, Anand
• 通讯作者: Rangarajan, Anand

Long-Range Multi-Object Tracking at Traffic Intersections on Low-Power Devices

低功耗设备上交通路口的远程多目标跟踪

• DOI: 10.1109/tits.2021.3115513
• 发表时间: 2022
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Emami, Patrick;Elefteriadou, Lily;Ranka, Sanjay
• 通讯作者: Ranka, Sanjay

TQAM: Temporal Attention for Cycle-wise Queue Length Estimation using High-Resolution Loop Detector Data

TQAM：使用高分辨率循环检测器数据进行循环队列长度估计的时间注意力

• DOI: 10.1109/itsc48978.2021.9564900
• 发表时间: 2021
• 期刊: Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference (ITSC
• 作者: Sengupta, Rahul;Karnati, Yashaswi;Rangarajan, Anand;Ranka, Sanjay
• 通讯作者: Ranka, Sanjay