Crowd Eye: Real-Time Human & Stampede Detection

by Rajiv Sharma 48 views

Hey guys! Let's dive into an exciting project that uses AI to make crowded spaces safer. We're talking about a real-time human detection and stampede prediction system, affectionately nicknamed "Crowd Eye." This system leverages the power of YOLOv8 to detect people in video feeds and predict potential stampede risks based on crowd density. It’s like having a super-smart, AI-powered security guard watching over things!

🚀 Overview

The core idea behind Crowd Eye is simple but powerful: use YOLOv8 for real-time human detection and then use that data to predict stampede risk based on how dense the crowd is. The system analyzes video frames, detects individuals, and then checks if the crowd size exceeds a predefined threshold. If it does, bam! The system flags a potential stampede risk. Think of it as an early warning system for crowded events, helping to prevent dangerous situations before they escalate.

This system is designed to provide crucial information in real-time, giving security personnel and event organizers the insight they need to manage crowds effectively and ensure the safety of everyone involved. It’s a proactive approach to crowd management, using technology to anticipate and mitigate risks.

The beauty of Crowd Eye lies in its adaptability and ease of use. It can be deployed in a variety of settings, from concert venues and sports stadiums to shopping malls and public transportation hubs. The ability to monitor crowd density in real-time opens up a world of possibilities for enhancing safety and security in crowded environments. And with its customizable parameters, Crowd Eye can be tailored to meet the specific needs of different locations and events.

🔥 Features

Let's break down the awesome features that make Crowd Eye tick:

  • ✅ Real-time human detection using YOLOv8: This is the heart of the system. YOLOv8 is a cutting-edge object detection model known for its speed and accuracy. It allows Crowd Eye to identify and track people in real-time, even in crowded scenes.
  • ✅ Custom threshold to predict stampede risk: This is where the smart prediction comes in. You can set a threshold for the maximum number of people allowed in a certain area. If the detected people count exceeds this threshold, the system will flag a potential stampede risk. This threshold can be adjusted based on the specific location and event.
  • ✅ Works with both images and videos: Flexibility is key! Crowd Eye can analyze both static images and live video feeds. This means you can use it to monitor security camera footage, analyze images from social media, or even process live streams.
  • ✅ Supports live webcam feed for real-time monitoring: Want to keep an eye on things as they happen? Crowd Eye can connect to a live webcam feed, allowing for real-time monitoring of crowd density. This is perfect for event organizers or security personnel who need to stay informed about the situation on the ground.
  • ✅ Easy-to-customize parameters: No need to be a tech whiz! Crowd Eye is designed to be user-friendly and easy to customize. You can adjust parameters like the crowd density threshold, the detection sensitivity, and the display settings to suit your specific needs.

🛠️ Steps to Run the Project

Okay, let's get into the nitty-gritty of how to get Crowd Eye up and running. Don't worry, it's not as complicated as it sounds! We'll break it down into simple steps:

Step 1️⃣: Setting Up YOLO Environment

First things first, you'll need to set up your environment to work with YOLOv8. This involves installing Python and a few necessary libraries.

  • Install Python (Recommended: Python 3.8+): If you don't already have Python installed, head over to the official Python website and download the latest version (3.8 or higher is recommended). Python is the language we'll be using to run Crowd Eye.

  • Install required dependencies using pip: Pip is a package installer for Python, and it makes it super easy to install the libraries we need. Open your command prompt or terminal and run the following command:

    pip install ultralytics opencv-python numpy

    This will install three key libraries:

    • ultralytics: This is the library that provides the YOLOv8 functionality.
    • opencv-python: OpenCV is a powerful library for computer vision tasks, including image and video processing.
    • numpy: NumPy is a library for numerical computing in Python, and it's used extensively in image and video processing.

Step 2️⃣: Choosing the Right Model

YOLOv8 comes in several different versions, each with its own trade-offs between speed and accuracy. For Crowd Eye, we need a model that's fast enough to run in real-time, but also accurate enough to detect people reliably.

  • We chose YOLOv8n (Nano Version) as it is fastest and lightweight, making it ideal for real-time detection on low-end machines. The "n" stands for "nano," and it's the smallest and fastest version of YOLOv8. This makes it perfect for running on computers with limited processing power, like laptops or embedded systems.
  • Other available versions: YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x (larger models provide better accuracy but are slower). If you have a more powerful machine, you might want to consider using one of the larger versions of YOLOv8. These models (s, m, l, and x) offer higher accuracy, but they also require more processing power and will run slower. The choice of model depends on your specific needs and the hardware you're using.

Step 3️⃣: Importing Required Libraries

Now that we have our environment set up, let's write some code! The first step is to import the necessary libraries into our Python script.

  • In your Python script, import the necessary modules:

    import cv2
from ultralytics import YOLO
    • cv2: This imports the OpenCV library, which we'll use for image and video processing.
    • from ultralytics import YOLO: This imports the YOLO class from the ultralytics library, which is what we'll use to load and run our YOLOv8 model.

Step 4️⃣: Processing Human Detection on an Image

Okay, now for the fun part: actually detecting people in an image!

  • Load the YOLO model:

    model = YOLO("yolov8n.pt")

    This line of code creates a YOLO object, which loads the YOLOv8n model weights from the "yolov8n.pt" file. This file contains the pre-trained weights for the model, which allow it to recognize different objects, including people.

  • Read the image and run detection:

    image = cv2.imread("image.jpg")
results = model(image)
    • image = cv2.imread("image.jpg"): This line reads an image from the file "image.jpg" using the cv2.imread() function and stores it in the image variable. Make sure you have an image file named "image.jpg" in the same directory as your Python script, or you can specify the full path to the image file.
    • results = model(image): This is where the magic happens! This line passes the image to the YOLO model, which performs object detection and returns the results. The results variable will contain information about the objects detected in the image, including the bounding boxes around the detected people.

  • Draw bounding boxes around detected humans: Now that we have the detection results, we can draw bounding boxes around the detected people to visualize the results. This typically involves iterating over the results and using OpenCV functions to draw rectangles on the image.

Step 5️⃣: Setting Up a Threshold for Stampede Prediction

This is where we add the stampede prediction logic to our system. We'll define a threshold for the number of people allowed in a certain area, and if the detected people count exceeds this threshold, we'll flag a potential stampede risk.

  • Define a crowd threshold (e.g., 16 people). This is the maximum number of people you want to allow in a certain area before the system raises an alert. The appropriate threshold will depend on the specific location and event. For example, a smaller space might require a lower threshold.

  • If the detected people count exceeds this threshold, the system raises a stampede risk alert. This is the core logic of the stampede prediction system. The system compares the detected people count to the threshold, and if the count is higher, it triggers an alert.

  • The output video frame displays:

    • People count
    • Stampede risk level (LOW/HIGH)

    To provide clear and actionable information, the system displays the current people count and the stampede risk level (LOW or HIGH) on the output video frame. This allows security personnel to quickly assess the situation and take appropriate action.

  • Code Snippet:

    CROWD_THRESHOLD = 16
def predict_stampede(people_count):
    return people_count > CROWD_THRESHOLD # High risk if count exceeds threshold

    This code snippet shows a simple way to implement the stampede prediction logic. We define a constant CROWD_THRESHOLD to represent the maximum allowed people count, and then we define a function predict_stampede() that takes the detected people count as input and returns True if the count exceeds the threshold (indicating a high stampede risk) and False otherwise.

📩 Future Enhancements

Crowd Eye is already pretty awesome, but there's always room for improvement! Here are some exciting enhancements we could add in the future:

  • 🔹 Integrate an alert system (sound or email notifications): Imagine Crowd Eye automatically sending an email or playing a sound alert when a stampede risk is detected. This would provide even faster and more direct notification to security personnel.
  • 🔹 Improve accuracy using YOLOv8m/l models for better detection: As we discussed earlier, the larger versions of YOLOv8 offer higher accuracy. If we want to squeeze even more performance out of Crowd Eye, we could explore using YOLOv8m or YOLOv8l, especially if we have access to more powerful hardware.
  • 🔹 Deploy as a web or mobile application for real-world monitoring: The ultimate goal is to make Crowd Eye as accessible and user-friendly as possible. Deploying it as a web or mobile application would allow security personnel and event organizers to monitor crowd density from anywhere, at any time.

Crowd Eye is a fantastic example of how AI can be used to create safer and more secure environments. By leveraging the power of YOLOv8 and real-time human detection, this system has the potential to make a real difference in crowd management and stampede prevention. It’s exciting to think about the possibilities for future development and the impact this technology could have on the world!