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A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras
- 1. A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras RoopTeja Muppalla, Sarasi Lalithsena, Tanvi Banerjee, Amit Sheth Kno.e.sis Center Wright State University, Dayton, OH Ohio Center of Excellence in Knowledge-Enabled Computing Industrial Knowledge Graph, Web Science ’17, Troy, NY, USA
- 2. Traffic Sensing • Traffic management has become a high priority to improve the traffic. 2
- 3. Related work in the semantic web • STAR-CITY presents a system which uses heterogeneous data sources for traffic analysis. • Issa et al., proposed an approach to semantically analyse information from a GPS tracker. • Susel et al., came up an ontology-based architecture to improve traffic. 3
- 4. Traffic Camera • Image processing and video processing have been used to perform traffic analysis. 4
- 5. Overview • Utilizing stationary traffic cameras as sensors with a semantic layer. • Represent and populate image features in a knowledge graph framework. • Extract actionable information to represent dynamic traffic conditions. 5
- 6. Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework 6 Data Collection Image Feature Extraction Virtuoso Triple Store Knowledge Graph Population
- 7. Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework 7 Data Collection Image Feature Extraction Virtuoso Triple Store Knowledge Graph Population
- 8. Data Collection • With the help of 511ny, 1100 camera URLs were accessed in and around New York city. 8
- 9. Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework 9 Data Collection Image Feature Extraction Virtuoso Triple Store Knowledge Graph Population
- 10. Feature Extraction – Median Image 10 Real – time Camera instances Median Image
- 11. Feature Extraction – Background Subtraction 11 Real – time Camera instances Median Subtraction
- 12. Feature Extraction – Manhattan Distance • We perform Manhattan distance1 to quantify the resulting image. 12 1. https://en.wikipedia.org/wiki/Sum_of_absolute_differences
- 13. Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework 13 Data Collection Image Feature Extraction Virtuoso Triple Store Knowledge Graph Population
- 14. Image feature annotation using knowledge graph framework ITSKG • Incorporate traffic imagery data in a knowledge graph. • Capability to integrate the with other types of sensor information. • Annotate and publish image data in the context of traffic events. 14
- 15. Semantic Modelling - ITSKG 15 • We adopt and extend the W3C Semantic Sensor Network (SSN) ontology to describe the traffic imagery information.
- 16. Example of raw imagery output to its equivalent RDF triples LOCATION: (-73.93, 40.80) URL: https://roo HOURLY CHANGE: 23.60 QUARTERLY CHANGE: 26.87 CLARIFAI TAGS: TRAFFIC JAM TIMESTAMP: 09-20-16, 9:48 DIMENSION: 352x240
- 17. Use Case • To evaluate whether the system is efficient enough in sensing the traffic, we verified with an observation made by 511ny. 17 “Update: Closure on #WillisAvenueBridge from Manhattan Side to Bronx Side” at 12:45 pm on 27 Oct, 2017.
- 18. Results • A threshold was set to filter the images using Virtuoso SPARQL endpoint. 18 Sample images returned from hourly change query Sample images returned from quarterly change query
- 19. Evaluation 19 • Clarifai API was used as our baseline to evaluate our system. Query Precision Recall F1 Score Quarterly Change 0.69 0.77 0.73 Hourly Change 0.63 0.77 0.69 Clarifai API 0.4 0.65 0.5
- 20. Evaluation 20 • Instances where ITSKG system was better a sensing the traffic compared to Clarifai API.
- 21. Conclusion • ITSKG framework has the potential to identify dynamic traffic conditions from camera imagery. • This framework is also well integrated with the existing Semantic Sensor Network (SSN). 21
- 22. Future Work • Address the limitations of traffic cameras. • Advanced image processing algorithms. • Social media features. 22
- 23. Thanks 23
- 24. References [1] Lécué, Freddy, et al. "Semantic traffic diagnosis with star-city: Architecture and lessons learned from deployment in dublin, bologna, miami and rio." International Semantic Web Conference. Springer International Publishing, 2014. [2] Fernandez, Susel, et al. "Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network." Sensors 16.8 (2016): 1287. [3] Hassan Issa,Ludger van Elst, and Andreas Dengel, “Using smartphones for prototyping semantic sensor analysis systems.” Proceedings of the International Workshop on Semantic Big Data. ACM, 2016. 24
Editor's Notes
- Good afternoon all, Myself is roopteja and I am grad student at wright state university, ohio Today I will be presenting on a knowledge graph framework for detecting traffic events using stationary cameras.
- With the increasing population of people in larger cities, the infrastructure of cities, such as the road networks, are overwhelmed with growing amounts of congestion as well as adverse events such as accidents. It is important to detect incidents such as accidents, closures etc., to reduce the impact on traffic.
- STAR-CITY presents a system which uses heterogeneous data sources as journey travel times, bus dynamics, social media feeds and event to support traffic analysis. Issa et al proposed an approach that uses smartphones for transforming objects or devices into semantic sensor data sources and providing the means to semantically analyze the captured data. Susel et al., proposed an ontology-based architecture to improve the driving environment through a traffic sensor network. All these studies explored the use of semantics in analysing the traffic, however they have not considered an important component for traffic which are images. And that is the novelty we bring in here.
- Researchers have done an extensive work to extract traffic information using various devices, such as magnetic loop sensors, radar, infrared detectors, cameras, etc. Among all these devices, video cameras are considered as being a suitable sensor device for capturing and recognizing spatio-temporal aspects of road structures and traffic situations. Many studies have focused on tracking vehicles and detecting objects in traffic.
- With this motive, we propose a technique to utilize stationary traffic cameras as sensors with a semantic layer to understand traffic patterns. We extract features that represent the dynamic traffic conditions from the camera imagery and we propose a way to incorporate traffic imagery data in a knowledge graph which allows integrating the traffic imagery data with other types of sensor information. We then demonstrate the use of the imagery features extracted and the knowledge graph developed for actionable information to represent dynamic traffic conditions such as congestion
- These cameras generate traffic images every 10 - 30 seconds.
- Median filtering is used to get the constant pixels from the images. We could also use mean but median filtering is widely used as it is very effective at removing noise while preserving edges.
- make use of simple approach called as background subtraction also known as foreground detection where image’s foreground is extracted for processing. With this we can observe the variability in the traffic. Room example
- Talk about the 24 hourly and 4 quarterly medians and explain the figure
- The use of semantic web techniques enable us to define or describe a domain model for a given domain and improves the understanding of the domain. Furthermore, these data can be easily used for analytical and intelligent applications. We propose a way to incorporate traffic imagery data in a knowledge graph which allows integrating the traffic imagery data with other types of sensor information. We create a knowledge graph framework using Semantic Web techniques to annotate and publish image data collected by various means in the context of traffic events.
- We adopt and extend the W3C semantic sensor network (SSN) ontology to describe the traffic imagery information. SSN ontology provides sensors, sensor observations and knowledge of the environment. We consider a traffic camera as a sensing device, camera image as a sensor output and traffic as a observation. SSN serves as an upper level schema to the Multi Model knowledge graph for Sensing Traffic Imagery.
- A sample example of the raw data and its equivalent RDF triple can be seen here.
- We focused on the cameras near Yankee stadium and the use case is focused on one particular traffic camera at 2nd Avenue 125st, Manhattan where an event has occurred on October 27, 2016
- Three annotators independently labeled 60 images from noon to 1 pm as either high congestion or low congestion. the performance using the Clarifai tags is not very promising.
- Our proposed ITSKG framework has the potential to identify dynamic traffic conditions from camera imagery as shown in our example use case. This framework is well integrated with the existing Semantic Sensor Network (SSN) and aids in analyzing heterogeneous streams of sensor data to extract meaningful information.
- Thanks for listening to the talk. I am now open for questions.