IN-202541031892-A - CONVOLUTIONAL NEURAL NETWORK (CNN) BASED VEHICLE SPEED DETECTION WITH VIDEO SURVEILLANCE

Abstract

Vehicle speed detection plays a crucial role in traffic management, safety enforcement, and monitoring systems. Traditional methods of speed detection, such as radar guns and induction loops, are often limited in scope, efficiency, and scalability. With the growing demand for smart transportation systems, the integration of machine learning (ML) offers a more advanced, flexible, and cost-effective solution for real-time speed detection.This explores the use of machine learning techniques for vehicle speed detection using video surveillance data. The proposed approach leverages computer vision and deep learning algorithms to analyze real-time footage, estimate vehicle speeds, and classify traffic conditions. By employing Convolutional Neural Networks (CNNs) for feature extraction and regression models for speed prediction, the system is capable of accurately estimating the velocity of moving vehicles without relying on physical sensors. The approach includes pre-processing steps such as object detection and tracking to identify individual vehicles, followed by speed estimation based on vehicle displacement across frames and the time intervals between them. Final Results show that the machine learning-based system offers comparable or superior accuracy to traditional methods while providing the flexibility to be implemented in various environments, such as highways, urban roads, and parking lots. Furthermore, the system's ability to continuously learn and adapt to new data makes it an attractive option for dynamic and evolving traffic monitoring scenarios. This concludes that machine learning offers significant advantages for vehicle speed detection, paving the way for more efficient, scalable, and automated traffic management solutions.

Inventors

• MRS SABINA PARVEEN
• DR S V DIVYA
• ROSHAN M
• SUGUMAR M
• SRIDHAR R

Dates

Publication Date

20250411

Application Date

20250331

Priority Date

20250331

Claims (10)

1. Claims:1.Improved Accuracy: The machine learning-based system provides highly accurate vehicle speed estimation, leveraging advanced computer vision and deep learning techniques, which can outperform traditional methods like radar guns and speed cameras.
2. 2.Real-time Detection: The approach supports real-time vehicle speed detection and monitoring, enabling immediate response to speed violations or traffic anomalies, thus improving the efficiency of traffic management.
3. 3.Non-invasive and Scalable: Unlike physical sensors such as radar or induction loops, the system relies on existing video surveillance infrastructure, making it a non-invasive solution that is scalable and adaptable to various locations with minimal setup.
4. 4.Cost-effective: By utilizing existing camera networks and machine learning algorithms, the proposed system reduces the need for costly specialized hardware and maintenance, making it a cost-effective solution for large-scale deployment in urban and highway environments.
5. 5.Flexibility in Different Environments: The machine learning model is designed to adapt to various traffic conditions and environments, including highways, city streets, and parking lots, making it versatile across diverse use cases.
6. 6.Automatic Speed Enforcement: With integrated machine learning models, the system can automatically detect and record speed violations, supporting automated enforcement systems and reducing the burden on law enforcement agencies.
7. 7.Continuous Learning and Adaptation: The machine learning model is capable of continuous learning, improving its performance and accuracy as it receives more data, making the system adaptable to new traffic patterns, road conditions, and vehicle types over time.
8. 8.Real-time Data Analytics: The system provides valuable data analytics, such as average speed, traffic density, and peak traffic times, helping authorities in making informed decisions about road maintenance, traffic policies, and infrastructure planning.
9. 9.Reduced Human Error: Unlike manual speed detection methods, the machine learning approach minimizes human error in speed measurement, providing a more reliable and objective solution for speed enforcement.
10. 10.Environmentally Friendly: The use of existing surveillance cameras reduces the need for additional physical infrastructure and the associated environmental impact of producing and installing new speed detection hardware.

Description

Complete SpecificationDescription:Introduction:Vehicle speed detection plays a vital role in ensuring road safety, managing traffic, and enforcing speed regulations. Traditional methods such as radar guns, speed cameras, and induction loops have been widely used but often come with limitations in terms of cost, scalability, and maintenance. With the rapid advancements in machine learning and computer vision, there is an opportunity to create more efficient, cost-effective, and scalable solutions for real-time vehicle speed detection. This project proposes the development of a vehicle speed detection system using machine learning algorithms, leveraging existing video surveillance infrastructure to detect, track, and estimate the speed of vehicles.The primary goal of this project is to provide a reliable and automated system for monitoring traffic conditions and enforcing speed regulations without the need for specialized hardware. By using deep learning-based object detection and tracking techniques, the system can detect moving vehicles and estimate their speed based on frame-to-frame displacement and time intervals. The proposed methodology aims to offer an accurate, flexible, and non-invasive solution to speed monitoring.Proposed Methodology:Data Collection: The system uses footage from existing video surveillance cameras, such as those installed along highways, streets, or intersections. The footage is used to identify and track vehicles in real-time.Object Detection: A deep learning-based object detection model, such as a Convolutional Neural Network (CNN) or a pre-trained model like YOLO (You Only Look Once) or Faster R-CNN, is used to identify vehicles in each frame of the video. This helps in distinguishing vehicles from other objects in the scene.Object Tracking: Once vehicles are detected, the system employs object tracking algorithms (e.g., Kalman Filter or Deep SORT) to track the motion of each vehicle across consecutive frames. This allows for the estimation of the vehicle's position over time.Fig.1: Architectural Diagram of CNN based Vehicle Speed DetectionSpeed Estimation: The speed of the detected vehicle is calculated based on the displacement between consecutive frames, combined with the known time difference between frames. By converting pixel displacement into real-world distance (using camera calibration), the system can compute the vehicle's actual speed.Real-time Processing: The model is designed for real-time processing, ensuring that vehicle speeds can be detected and monitored immediately, making the system suitable for dynamic environments and rapid traffic monitoring.Post-Processing & Analysis: After detecting and estimating speeds, the system can generate real-time traffic analytics, including average speeds, traffic density, and violations. This data can be used by traffic management authorities for decision-making.Regulated Power Supply:For the vehicle speed detection and pothole detection system, a regulated power supply ensures stable and consistent voltage to all components of the system, including cameras, sensors, and computing units. This is crucial for maintaining the accuracy of the system and preventing malfunction due to power surges or instability.Power Requirements: The system's components, such as surveillance cameras, machine learning processors, and data storage units, require a steady and regulated power supply to function efficiently.Power Supply Design: A regulated power supply can be achieved using voltage regulators and uninterruptible power supply (UPS) systems to prevent data loss in case of power failures and ensure continuous operation of the system. Pothole Detection Working Process:Data Collection (Video/Images): Surveillance cameras or vehicle-mounted cameras capture video footage of the road surface, focusing on potholes, road irregularities, and other defects.Preprocessing and Image Enhancement: The captured footage undergoes preprocessing, which includes noise reduction, image enhancement, and contrast adjustment. This step helps improve the quality of the data for better detection accuracy.Object Detection Using ML Models: A deep learning model (such as YOLO or Faster R-CNN) is trained to detect potholes. The model classifies potholes based on patterns in the road surface, such as depression, cracks, and surface irregularities. Localization and Size Estimation: After detection, the system estimates the pothole's location and size by drawing bounding boxes and calculating the area in the image, allowing for effective reporting and management of road maintenance.Validation and Testing:Validation and testing ensure that the vehicle speed detection and pothole detection system works as intended, providing reliable results under various conditions.Testing in Real-world Scenarios: The system is tested using actual road footage, both in urban and highway settings, to ensure that the vehicle speed detection and pothole detection components function effect