# LiVeR: Lightweight Vehicle Detection and Classification in Real-Time

arXiv: https://arxiv.org/abs/2206.06173 · HTML: https://mlsanity.com/paper/2206.06173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 7 of 7)

- [YOLACT++: Better Real-time Instance Segmentation](https://mlsanity.com/paper/1912.06218/) (2019)
- [Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services](https://mlsanity.com/paper/2001.08557/) (2020)
- [DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes](https://mlsanity.com/paper/2205.08207/) (2022)
- [Radio-based Traffic Flow Detection and Vehicle Classification for Future Smart Cities](https://mlsanity.com/paper/1801.03317/) (2018)
- [DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning](https://mlsanity.com/paper/1812.08208/) (2018)
- [BlueFlood: Concurrent Transmissions for Multi-Hop Bluetooth 5 -- Modeling and Evaluation](https://mlsanity.com/paper/2002.12906/) (2020)
- [The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting](https://mlsanity.com/paper/2003.09827/) (2020)

## Abstract (arXiv)

Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet) connectivity, as well as adequate uninterrupted power-supply. Such dependencies make these solutions fundamentally impractical considering the possible adversities of outdoor environment as well as requirement of correlated wide-area operation. For practical use, apart from being technically sound and accurate, a solution has to be lightweight, cost-effective, easy-to-install, flexible as well as supporting efficient time-correlated coverage over large area. In this work we propose an IoT-assisted strategy to fulfil all these goals together. We adopt a top-down approach where we first introduce a lightweight framework for time-correlated low-cost wide-area measurement and then reuse the concept for developing the individual measurement units. Our extensive outdoor measurement studies and trace-based simulation on the empirical data show about 98% accuracy in vehicle detection and upto 93% of accuracy in classification of the vehicles over moderately busy urban roads.