Paper Info
Title
Cosmos Smart Intersection: Edge Compute And Communications For Bird'S Eye Object Tracking
Abstract
Smart-city intersections will play a crucial role in automated traffic management and improvement in pedestrian safety in cities of the future. They will (i) aggregate data from in-vehicle and infrastructure sensors; (ii) process the data by taking advantage of low-latency high-bandwidth communications, edge-cloud computing, and AI-based detection and tracking of objects; and (iii) provide intelligent feedback and input to control systems. The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) enables research on technologies supporting smart cities. In this paper, we provide results of experiments using bird's eye cameras to detect and track vehicles and pedestrians from the COSMOS pilot site. We assess the capabilities for real-time computation, and detection and tracking accuracy - by evaluating and customizing a selection of video pre-processing and deep-learning algorithms. Distinct issues that are associated with the difference in scale for bird's eye view of pedestrians vs. cars are explored and addressed: the best multiple-object tracking accuracies (MOTA) for cars are around 73.2, and around 2.8 for pedestrians. The real-time goal of 30 frames-per-second - i.e., a total of 33.3 ms of latency for object detection for vehicles will be reachable once the processing time is improved roughly by a factor of three.
Year
DOI
Venue
2020
10.1109/PerComWorkshops48775.2020.9156225
PerCom Workshops
Keywords
DocType
ISSN
smart-city, smart-intersection, deep learning, detection, tracking, testbed, wireless
Conference
2474-2503
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Shiyun Yang100.34
Emily Bailey200.34
Zhengye Yang300.34
Jonatan Ostrometzky4215.52
Gil Zussman5116.07
Ivan Seskar697796.57
Zoran Kostic700.34