A Knowledge Graph Framework for Detecting Traff€ic Events Using Stationary Cameras

TitleA Knowledge Graph Framework for Detecting Traff€ic Events Using Stationary Cameras
Publication TypeConference Paper
Year of Publication2017
AuthorsRoopteja Muppalla, Sarasi Lalithsena, Tanvi Banerjee, Amit Sheth
Conference NameIndustrial Knowledge Graphs 2017 Workshop (co-located with 9th International ACM Web Science Conference 2017)
Date Published06/25/2017
Conference LocationTroy, NY
KeywordsKnowledge graphs, Traffic events, Traffic image feature extraction
Abstract

With the rapid increase in urban development, it is critical to utilize dynamic sensor streams for traffic understanding, especially in larger cities where route planning or infrastructure planning is more critical. This creates a strong need to understand traffic patterns using ubiquitous sensors to allow city officials to be better informed when planning urban construction and to provide an understanding of the traffic dynamics in the city. In this study, we propose our framework ITSKG (Imagery-based Traffic Sensing Knowledge Graph) which utilizes the stationary traffic camera information as sensors to understand the traffic patterns. The proposed system extracts image-based features from traffic camera images, adds a semantic layer to the sensor data for traffic information, and then labels traffic imagery with semantic labels such as congestion. We share a prototype example to highlight the novelty of our system and provide an online demo to enable users to gain a better understanding of our system. This framework adds a new dimension to existing traffic modeling systems by incorporating dynamic image-based features as well as creating a knowledge graph to add a layer of abstraction to understand and interpret concepts like congestion to the traffic event detection system.

DOI10.475/123 4
Full Text

Full-text Citation:
Muppalla, R., Lalithsena, S., Banerjee, T., and Sheth, A. A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras. In Proceedings of Industrial Knowledge Graphs 2017 Workshop - ACM Web Science 2017 Conference, Troy, NY USA, June 2017. DOI: 10.475/123-4.

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