Traffic intersections are prime locations for deployment of infrastructure sensors and edge computing nodes to
realize the vision of a smart city. It is expected that the needs
of a smart city, in regards to traffic and pedestrian traffic
systems monitored by cameras/video, can be met by using stateof-the-art artificial-intelligence (AI) based object detectors and
trackers. A critical component in designing an effective real-time
object detection/tracking pipeline is the understanding of how
object density, i.e., the number of objects in a scene, and imageresolution and frame rate influence the performance metrics. This
study explores the accuracy and speed metrics with the goal of
supporting pipelines that meet the precision and latency needs
of a real-time environment. We examine the impact of varying
image-resolution, frame rate and object-density on the object
detection performance metrics. The experiments on the COSMOS
testbed dataset show that varying the frame width from 416 pixels
to 832 pixels, and cropping the images to a square resolution,
result in the increase in average precision for all object classes.
Decreasing the frame rate from 15 fps to 5 fps preserves more
than 90% of the highest F1 score achieved for all object classes.
The results inform the choice of video preprocessing stages,
modifications to established AI-based object detection/tracking
methods, and suggest optimal hyper-parameter values.
Index Terms—Object Detection, Smart City, Video Resolution,
Deep Learning Models.
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WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes
Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.
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- NSF-PAR ID:
- 10420199
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Internet of Things
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2691-1914
- Page Range / eLocation ID:
- 1 to 23
- 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/10.1145/3549551
