To create an intelligent object detection system capable of processing video streams, identifying potential objects, and marking them dynamically using template matching.
Features
Edge Detection:
Implements edge detection to isolate features of interest within video frames.
Hough Line Transform:
Identifies lines and edges, which are critical for detecting objects.
Template Matching:
Compares video features with predefined object templates to ensure precise detection.
Real-Time Feedback:
Dynamically marks detected objects in the video, providing immediate visual feedback.
Frame-by-Frame Processing:
Processes the video in real-time, ensuring no frames are skipped.
Prerequisites
Python 3.x
OpenCV library
Setup and Installation
Ensure Python is installed on your system.
Install the required library by running:
pip install opencv-python
Download the object_detection_video.py script and ensure a compatible video file (e.g., lane1.avi) is in the same directory.
How It Works
Input Video Stream: The program processes a video file as input.
Edge and Line Detection:
Performs edge detection on frames to isolate potential objects.
Applies the Hough Line Transform to identify lines that help locate objects.
Template Matching:
Matches detected features with predefined object templates.
Object Marking:
Marks detected objects directly on the video frames and displays the output in real time.
Applications
Traffic Surveillance: Detect vehicles or objects on roads.
Automation Systems: Recognize objects in manufacturing or logistics.
Intelligent Vehicles: Assist with object detection in autonomous systems.
Video Analytics: Enhance object detection in security footage.
Project Files
object_detection_video.py: The main script for processing and detecting objects in video streams.
Sample Video:lane1.avi for testing object detection.
Screenshots: Example outputs:
Screenshot 2024-01-20 191737.png
Screenshot 2024-01-20 191904.png
Other examples: c1.png, c2.png, c3.png, c4.png, c5.png, s1.png, s2.png.
