The density and complexity of urban environments
present significant challenges for autonomous vehicles. Moreover,
ensuring pedestrians’ safety and protecting personal privacy
are crucial considerations in these environments. Smart city
intersections and AI-powered traffic management systems will be
essential for addressing these challenges. Therefore, our research
focuses on creating an experimental framework for the design of
applications that support the secure and efficient management of
traffic intersections in urban areas. We integrated two cameras
(street-level and bird’s eye view), both viewing an intersection,
and a programmable edge computing node, deployed within the
COSMOS testbed in New York City, with a central management
platform provided by Kentyou. We designed a pipeline to collect
and analyze the video streams from both cameras and obtain
real-time traffic/pedestrian-related information to support smart
city applications. The obtained information from both cameras
is merged, and the results are sent to a dedicated dashboard
for real-time visualization and further assessment (e.g., accident
prevention). The process does not require sending the raw videos
in order to avoid violating pedestrians’ privacy. In this demo, we
present the designed video analytic pipelines and their integration
with Kentyou central management platform.
Index Terms—object detection and tracking, camera networks,
smart intersection, real-time visualization
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Real-Time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy
Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could be used to accurately detect and locate pedestrians using deep learning, and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction.
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- Award ID(s):
- 1725573
- PAR ID:
- 10107258
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2673
- Issue:
- 6
- ISSN:
- 0361-1981
- Page Range / eLocation ID:
- 129 to 139
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0361198119843255
