# Real-time smart vehicle surveillance system

arXiv: https://arxiv.org/abs/2111.12289 · HTML: https://mlsanity.com/paper/2111.12289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector](https://mlsanity.com/paper/1909.01754/) (2019)
- [View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network](https://mlsanity.com/paper/1702.01721/) (2017)
- [Artificial Intelligence Enabled Traffic Monitoring System](https://mlsanity.com/paper/2010.01217/) (2020)
- [Vehicle Shape and Color Classification Using Convolutional Neural Network](https://mlsanity.com/paper/1905.08612/) (2019)

## Abstract (arXiv)

Over the last decade, there has been a spike in criminal activity all around the globe. According to the Indian police department, vehicle theft is one of the least solved offenses, and almost 19% of all recorded cases are related to motor vehicle theft. To overcome these adversaries, we propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed. The proposed system extracts various attributes of the vehicle such as Make, Model, Color, License plate number, and type of the license plate. Various image processing and deep learning algorithms are employed to meet the objectives of the proposed system. The extracted features can be used as evidence to report violations of law. Although the system uses more parameters, it is still able to make real time predictions with minimal latency and accuracy loss.