# Real-Time Detection and Analysis of Vehicles and Pedestrians using Deep Learning

arXiv: https://arxiv.org/abs/2404.08081 · HTML: https://mlsanity.com/paper/2404.08081/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)

## Built on by (most cited first, 5 of 5)

- [Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets](https://mlsanity.com/paper/2504.04292/) (2025)
- [Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime](https://mlsanity.com/paper/2503.11008/) (2025)
- [ECCO: Leveraging Cross-Camera Correlations for Efficient Live Video Continuous Learning](https://mlsanity.com/paper/2512.11727/) (2025)
- [Video Detector: A Dual-Phase Vision-Based System for Real-Time Traffic Intersection Control and Intelligent Transportation Analysis](https://mlsanity.com/paper/2603.14861/) (2026)
- [Real-Time Traffic Density Estimation: A Hybrid Computer Vision and Predictive Machine Learning Pipeline](https://mlsanity.com/paper/2610.14326/) (2026)

## Abstract (arXiv)

Computer vision, particularly vehicle and pedestrian identification is critical to the evolution of autonomous driving, artificial intelligence, and video surveillance. Current traffic monitoring systems confront major difficulty in recognizing small objects and pedestrians effectively in real-time, posing a serious risk to public safety and contributing to traffic inefficiency. Recognizing these difficulties, our project focuses on the creation and validation of an advanced deep-learning framework capable of processing complex visual input for precise, real-time recognition of cars and people in a variety of environmental situations. On a dataset representing complicated urban settings, we trained and evaluated different versions of the YOLOv8 and RT-DETR models. The YOLOv8 Large version proved to be the most effective, especially in pedestrian recognition, with great precision and robustness. The results, which include Mean Average Precision and recall rates, demonstrate the model's ability to dramatically improve traffic monitoring and safety. This study makes an important addition to real-time, reliable detection in computer vision, establishing new benchmarks for traffic management systems.