CANS: Communication Limited Camera Network Self-Configuration for
Intelligent Industrial Surveillance
- URL: http://arxiv.org/abs/2109.05665v1
- Date: Mon, 13 Sep 2021 01:54:33 GMT
- Title: CANS: Communication Limited Camera Network Self-Configuration for
Intelligent Industrial Surveillance
- Authors: Jingzheng Tu, Qimin Xu and Cailian Chen
- Abstract summary: Realtime and intelligent video surveillance via camera networks involve computation-intensive vision detection tasks with massive video data.
Multiple video streams compete for limited communication resources on the link between edge devices and camera networks.
An adaptive camera network self-configuration method (CANS) of video surveillance is proposed to cope with multiple video streams of heterogeneous quality of service.
- Score: 8.360870648463653
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Realtime and intelligent video surveillance via camera networks involve
computation-intensive vision detection tasks with massive video data, which is
crucial for safety in the edge-enabled industrial Internet of Things (IIoT).
Multiple video streams compete for limited communication resources on the link
between edge devices and camera networks, resulting in considerable
communication congestion. It postpones the completion time and degrades the
accuracy of vision detection tasks. Thus, achieving high accuracy of vision
detection tasks under the communication constraints and vision task deadline
constraints is challenging. Previous works focus on single camera configuration
to balance the tradeoff between accuracy and processing time of detection tasks
by setting video quality parameters. In this paper, an adaptive camera network
self-configuration method (CANS) of video surveillance is proposed to cope with
multiple video streams of heterogeneous quality of service (QoS) demands for
edge-enabled IIoT. Moreover, it adapts to video content and network dynamics.
Specifically, the tradeoff between two key performance metrics, \emph{i.e.,}
accuracy and latency, is formulated as an NP-hard optimization problem with
latency constraints. Simulation on real-world surveillance datasets
demonstrates that the proposed CANS method achieves low end-to-end latency (13
ms on average) with high accuracy (92\% on average) with network dynamics. The
results validate the effectiveness of the CANS.
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