# Precision-Aware Video Compression for Reducing Bandwidth Requirements in Video Communication for Vehicle Detection-Based Applications

arXiv: https://arxiv.org/abs/2508.02533 · HTML: https://mlsanity.com/paper/2508.02533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Deep Learning Based Video System for Accurate and Real-Time Parking Measurement](https://mlsanity.com/paper/1902.07401/) (2019)
- [Real-Time Accident Detection in Traffic Surveillance Using Deep Learning](https://mlsanity.com/paper/2208.06461/) (2022)
- [Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators](https://mlsanity.com/paper/2303.15231/) (2023)

## Abstract (arXiv)

Computer vision has become a popular tool in intelligent transportation systems (ITS), enabling various applications through roadside traffic cameras that capture video and transmit it in real time to computing devices within the same network. The efficiency of this video transmission largely depends on the available bandwidth of the communication system. However, limited bandwidth can lead to communication bottlenecks, hindering the real-time performance of ITS applications. To mitigate this issue, lossy video compression techniques can be used to reduce bandwidth requirements, at the cost of degrading video quality. This degradation can negatively impact the accuracy of applications that rely on real-time vehicle detection. Additionally, vehicle detection accuracy is influenced by environmental factors such as weather and lighting conditions, suggesting that compression levels should be dynamically adjusted in response to these variations. In this work, we utilize a framework called Precision-Aware Video Compression (PAVC), where a roadside video camera captures footage of vehicles on roadways, compresses videos, and then transmits them to a processing unit, running a vehicle detection algorithm for safety-critical applications, such as real-time collision risk assessment. The system dynamically adjusts the video compression level based on current weather and lighting conditions to maintain vehicle detection accuracy while minimizing bandwidth usage. Our results demonstrate that PAVC improves vehicle detection accuracy by up to 13% and reduces communication bandwidth requirements by up to 8.23x in areas with moderate bandwidth availability. Moreover, in locations with severely limited bandwidth, PAVC reduces bandwidth requirements by up to 72x while preserving vehicle detection performance.