YOLO Object Timing

This repository provides code to calculate the duration that objects remain within a defined region using YOLO. Based on the object counting code from Ultralytics, this project extends the functionality to include timing calculations.

Overview

YOLO Object Timing leverages the power of YOLO (You Only Look Once) for object detection and tracking. It adds a timing component to measure how long each detected object stays within a specified region in the video frame.

Features

• Object Detection: Detects objects using the YOLOv8 model.
• Region of Interest: Defines a specific region in the video frame to monitor.
• Object Timing Per Individual Object: Tracks and calculates the time for each individual object.
• Average Timing from All Objects: Computes the average time all objects spend in the defined region.
• Object Timing for Specific Object Using Tracking: Tracks and calculates the time for specific objects based on tracking ID.
• Real-time Processing: Processes video streams in real-time.
• Max Time Calculation: Calculates the maximum time any object has spent in the defined region.

How It Works

1. Initialization: The YOLO model is initialized and video input is set up.
2. Region Definition: A region of interest (ROI) is defined within the video frame.
3. Object Tracking: Objects are detected and tracked using YOLO.
4. Timing Calculation: For each detected object, the time spent within the ROI is calculated and displayed.

Installation

Clone the repository and install the required libraries:

git clone https://github.com/zanjabil2502/Yolo-Object-Timing.git
cd Yolo-Object-Timing
pip install -r requirements.txt

Usage

1. Define Your Region: Specify the coordinates of the region in the script.
2. Run the Script: Execute the script to start processing the video and timing objects.

import cv2
from ultralytics import YOLO
from src.object_timer import ObjectTimer

path_model = "yolov8n.pt" # You can change model
url = "Your source path" # You can use Video or Path CCTV Realtime

# Initialize YOLOv8 Detection Model
model = YOLO(path_model)
classes_to_count = [2]  # car classes for count
region_points = [(0, 600), (350, 400), (1280, 400), (1280, 960), (0, 960)]

# Open the Video File
cap = cv2.VideoCapture(url)
assert cap.isOpened(), "Error reading video file"
fps = cap.get(cv2.CAP_PROP_FPS)


# Initialize Object Timer
timer = ObjectTimer(
    fps=fps,  # FPS from you source video
    view_img=True,  # Display the image during processing
    reg_pts=region_points,  # Region of interest points
    names=model.names,  # Class names from the YOLO model
    draw_tracks=True,  # Draw tracking lines for objects
    line_thickness=2,  # Thickness of the lines drawn
)


# itterate over video frames:
frame_count = 0
while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print(
            "Video frame is empty or video processing has been successfully completed."
        )
        break

    # Write the annotated frame to the output video
    frame_count += 1

    # Perform object tracking on the current frame
    tracks = model.track(frame, persist=True, classes=classes_to_count)

    # Use the Object Timer to timing objects in the frame and get the annotated image
    frame = timer.start_timing(frame, tracks, frame_count)

# Release all Resources:
cap.release()
cv2.destroyAllWindows()

Example Images

Use Case Analysis

1. Analysis of Waiting Time or Queue at Toll Gates, Traffic Lights, and Cashiers

This system can be employed to analyze the waiting times at various points of interest:

• Toll Gates: Measure how long vehicles stay in the toll gate area, providing insights into congestion and processing efficiency.
• Traffic Lights: Calculate the duration vehicles or pedestrians wait at traffic lights, helping optimize traffic flow and signal timings.
• Cashiers: Track the time customers spend waiting at cashier lines in retail stores, aiding in staff allocation and queue management.

2. Analysis of Customer Visit Duration at Cafes or Restaurants

Monitor and analyze how long customers stay in designated areas within cafes or restaurants:

• Customer Behavior: Understand customer behavior and average visit durations to improve service and customer experience.
• Table Turnover: Measure the turnover rate of tables to optimize seating arrangements and service efficiency.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.