Leibniz-Zentrum für Informatik (Schloss Dagstuhl)· 2024
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
- 14,339citations
- 2024year
Short summary
A novel distributed IoT system achieves 77% vehicle detection accuracy and 99.4% emergency vehicle detection accuracy using a two-stage detector and acoustic siren detection on edge devices.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a novel distributed IoT framework for urban traffic control.
- Achieves 77% vehicle detection accuracy using a two-stage detector on the UA-DETRAC dataset.
- Detects emergency vehicles via acoustic siren analysis on edge devices with 99.4% average accuracy.
- Addresses limitations of existing systems, particularly the lack of emergency vehicle datasets for deep learning.
AI-generated from the title and abstract; the full text is not read.
Abstract
Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%.
The authors' abstract, as published at the source. Leibniz-Zentrum für Informatik (Schloss Dagstuhl), 2024 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science