Lane, Vehicle and Pedestrian Detection

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Lane, Vehicle and Pedestrian Detection

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Overview
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Detail Description

1. Abstract

Lane, Vehicle and Pedestrian Detection is a computer vision project designed to improve road scene understanding for intelligent transportation systems and autonomous vehicles. The system uses deep learning and image processing techniques to identify lane markings, detect vehicles, and recognize pedestrians from images or video streams. It combines lane detection algorithms with object detection models to analyze road boundaries, vehicle positions, and pedestrian locations. The system can process live camera feeds or recorded videos and display detection results with visual annotations. This project supports driver assistance, road safety analysis, and the development of intelligent autonomous driving applications.

2. Objectives

To detect lane markings from road images and video streams.
To identify vehicles such as cars, buses, trucks, and motorcycles.
To detect pedestrians in road environments.
To combine lane detection and object detection in a single system.
To process video frames and display detection results.
To evaluate model performance under different road and lighting conditions.
To support intelligent transportation and autonomous driving research.

3. Existing System

Traditional road monitoring systems rely on manual observation or separate computer vision methods for detecting lanes and vehicles. Some systems use basic image processing techniques, which may perform poorly when lane markings are faded, lighting changes, or roads contain complex backgrounds.

Disadvantages:

Limited accuracy in challenging lighting conditions.
Difficulty detecting faded or partially obscured lane markings.
Separate systems may be required for lanes, vehicles, and pedestrians.
Reduced performance in crowded or complex road scenes.
Difficulty handling occlusion, shadows, and adverse weather.

4. Proposed System

The proposed system combines lane detection algorithms and deep learning-based object detection to analyze road scenes. A lane detection module identifies road markings, while a model such as YOLO detects vehicles and pedestrians. The system overlays lane boundaries and object labels on video frames and can optionally estimate object positions relative to the detected lanes.

Key Features:

Lane marking and road boundary detection.
Vehicle detection and classification.
Pedestrian detection.
Real-time or recorded-video processing.
Bounding boxes, labels, and confidence scores.
Lane and object visualization.
Optional object tracking and lane-based vehicle counting.
Performance evaluation under different environmental conditions.

5. Implementation Procedure

Data Collection: Obtain road images and videos from suitable datasets, such as BDD100K, KITTI, or TuSimple, depending on the detection task.
Data Preprocessing: Resize images, normalize input data, and prepare annotations for lane markings, vehicles, and pedestrians.
Lane Detection: Implement a lane segmentation or lane detection model, or use image processing techniques such as edge detection and Hough transforms for a basic prototype.
Object Detection: Use a pretrained YOLO model or train a suitable object detector to identify vehicles and pedestrians.
Model Integration: Combine the lane detection and object detection outputs into a unified video-processing pipeline.
Video Processing: Read video frames using OpenCV and run the detection modules on each frame.
Visualization: Draw lane boundaries, object bounding boxes, class labels, and confidence scores on the output frames.
Optional Object Tracking: Integrate a tracker such as ByteTrack or SORT to maintain object identities across consecutive frames.
Dashboard Development: Create an interface to upload videos, view annotated output, and display detection statistics.
Testing and Evaluation: Evaluate object detection using precision, recall, and mean Average Precision (mAP), and assess lane detection using suitable lane-specific metrics.

6. Software Requirements

Component	Recommended Technology
Programming Language	Python
Computer Vision	OpenCV
Object Detection	YOLO
Deep Learning	PyTorch
Lane Detection	OpenCV or a lane segmentation model
Data Processing	NumPy, Pandas
Visualization	Matplotlib
Dashboard	Streamlit or Flask
Object Tracking (Optional)	ByteTrack or SORT
Development Environment	VS Code or Jupyter Notebook

7. Hardware Requirements

Component	Recommended Specification
Processor	Intel Core i5 or equivalent
RAM	8 GB minimum; 16 GB recommended
Storage	256 GB SSD or higher
GPU	NVIDIA GPU recommended for real-time processing
Camera	USB camera, dashcam, or recorded road video
Display	Monitor for viewing detection results
Edge Device (Optional)	NVIDIA Jetson or equivalent
Operating System	Windows or Linux

8. Advantages of the Project

Combines lane, vehicle, and pedestrian detection in one system.
Improves automated road scene understanding.
Supports driver assistance and autonomous driving research.
Helps analyze traffic flow and road-user positions.
Provides visual detection results for easier interpretation.
Can process live camera feeds or prerecorded videos.
Supports optional vehicle tracking and traffic statistics.
Can be extended for collision-warning and road-safety applications.

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