This project focuses on developing a state-of-the-art traffic monitoring system using monocular 3D object recognition techniques. The system leverages deep learning algorithms to accurately detect and monitor various road users, such as vehicles and pedestrians, using a single camera. This approach aims to provide a cost-effective and efficient solution for traffic monitoring in urban environments.
The main objectives of this project are:
- Develop a monocular 3D object detection model for traffic monitoring.
- Accurately identify and track different types of road users, including vehicles and pedestrians.
- Enhance traffic monitoring capabilities using cost-effective single-camera setups.
- Provide real-time traffic analysis to improve urban traffic management and safety.
- Deep Learning Model: Utilized a custom 3D object detection model to capture and process 3D information from a single camera.
- Bounding Box Estimation: The model estimates 3D bounding boxes around detected objects, providing accurate spatial information.
- Data Integration: Combined 3D data with 2D imagery to enhance object detection accuracy.
- Validation: Employed rigorous validation techniques to ensure the robustness and reliability of the detection model.
- Object Tracking: Implemented multi-object and multi-class tracking to follow detected objects over time.
- Auto-Calibration: Developed a novel auto-calibration technique to adjust the system for different camera setups and perspectives.
- Monocular 3D Detection: Capture 3D information using a single camera, reducing costs and complexity.
- Real-Time Monitoring: Provide real-time detection and tracking of various road users.
- Traffic Analysis: Analyze traffic flow, congestion, and safety in urban environments.
- Cost-Effective Solution: Deploy a scalable and affordable traffic monitoring system using existing surveillance infrastructure.
Input: A single camera setup monitoring a busy urban intersection.
System Output:
- 3D Object Detection: The system identifies vehicles and pedestrians in the scene, estimating their 3D positions and orientations.
- Object Tracking: Continuously tracks the detected objects, updating their positions in real-time.
- Traffic Analysis: Provides insights into traffic flow, congestion levels, and potential safety hazards.
Scenario Steps:
- Camera Setup: Install a single camera at the desired monitoring location.
- Data Capture: The camera captures continuous video feeds of the traffic scene.
- Model Application: Apply the 3D object detection model to the video feeds to identify and track road users.
- Real-Time Monitoring: Monitor traffic conditions in real-time, using the system's output to make informed decisions.
- Enhanced Safety: Improve traffic safety by providing accurate and real-time monitoring of road users.
- Efficiency: Streamline traffic management operations with advanced detection and tracking capabilities.
- Scalability: Easily scale the system to cover larger areas or more intersections using cost-effective single-camera setups.
To use the model:
- Ensure your environment is set up with the necessary tools (e.g., PyTorch).
- Load your video feeds into the specified directories.
- Run the preprocessing script to prepare the data for model input.
- Execute the object detection model to identify and track road users.
- Analyze the output for real-time 3d traffic monitoring and management.