[AI from Scratch] Episode 298: Anomaly Detection — Techniques for Detecting Anomalies in Surveillance Camera Footage

Recap and Today’s Theme

Hello! In the previous episode, we explored video data analysis, covering various methods for extracting information from videos and their applications. Video analysis is widely used in fields such as surveillance, sports analysis, video production, and autonomous driving.

Today, we’ll focus on anomaly detection, which involves detecting unusual events or behaviors in surveillance camera footage. Anomaly detection plays a key role in identifying suspicious behavior or incidents, such as unauthorized entry, falls, or violent acts. In this article, we’ll explain the basics of anomaly detection, its practical applications, and the associated challenges.

What is Anomaly Detection?

Anomaly detection refers to the automatic identification of actions or events that deviate from normal patterns. In the context of surveillance camera footage, it may detect events like trespassing, falls, or violence, and trigger an alarm to facilitate a prompt response.

Main Applications of Anomaly Detection

1. Security Surveillance: Used to detect suspicious individuals or intruders within a facility.
2. Public Safety: Detects dangerous situations, such as falls or fights, in public spaces like stations or parks.
3. Manufacturing Monitoring: Detects abnormal machine operations or movements on production lines to prompt maintenance.
4. Traffic Management: Identifies traffic accidents or illegal parking, improving traffic control.

Basic Methods of Anomaly Detection

Anomaly detection methods are broadly classified into rule-based methods and learning-based methods.

1. Rule-Based Anomaly Detection

In rule-based methods, anomalies are detected based on predefined criteria or rules. For example, an alarm can be triggered if a person enters a restricted area or exhibits specific behaviors like running or falling.

• Features:
• Simple and effective for specific scenarios with known patterns.
• Easy to implement, making it suitable for specialized anomaly detection tasks.
• Drawbacks:
• Limited flexibility when dealing with changing environments or diverse behaviors.
• Requires frequent updates to accommodate new types of anomalies.

2. Learning-Based Anomaly Detection

In recent years, deep learning-based anomaly detection has become the dominant approach. This method involves training a model on normal behavior patterns, with anything that deviates from these patterns being classified as an anomaly.

a. One-Class Classification Using Only Normal Data

This approach trains models using only normal data, making it useful in environments where anomalies are rare. Common models include Autoencoders and One-Class SVM.

• Autoencoder:
• A type of deep learning model that learns to reconstruct normal data. If an anomaly occurs, the model produces a large reconstruction error, which signals an anomaly.
• One-Class SVM:
• A variant of Support Vector Machines (SVM) that encloses normal data within a boundary, classifying data points outside the boundary as anomalies.

b. Anomaly Detection with Labeled Anomaly Data

When anomaly data is available, supervised learning methods, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can be used to learn patterns of both normal and anomalous behaviors.

• YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector):
• Detect specific actions (e.g., a person falling or engaging in violent behavior) in real-time video footage.
• LSTM (Long Short-Term Memory):
• Analyzes temporal sequences to detect unusual motion patterns over time.

Example of Anomaly Detection in Python

In Python, anomaly detection can be implemented using libraries like OpenCV and TensorFlow. Below is a simple example of detecting motion anomalies using background subtraction.

Required Libraries

pip install opencv-python

Motion Detection Using Background Subtraction

The following code demonstrates how to detect anomalies (motion) in real-time camera footage using background subtraction.

import cv2

# Capture video from the camera
cap = cv2.VideoCapture(0)

# Create a background subtractor
fgbg = cv2.createBackgroundSubtractorMOG2()

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Apply background subtraction
    fgmask = fgbg.apply(frame)

    # Detect contours
    contours, _ = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for contour in contours:
        # Detect if the contour area is above a certain threshold
        if cv2.contourArea(contour) > 1000:
            x, y, w, h = cv2.boundingRect(contour)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.putText(frame, "Anomaly Detected", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    # Display the results
    cv2.imshow('Anomaly Detection', frame)

    if cv2.waitKey(30) & 0xFF == 27:  # Exit with ESC key
        break

cap.release()
cv2.destroyAllWindows()

Code Explanation

• cv2.VideoCapture(): Captures real-time video from the camera for analysis.
• createBackgroundSubtractorMOG2(): Uses a background subtractor to extract moving objects from the scene.
• cv2.findContours(): Detects contours (shapes) in the frame, drawing rectangles around them if they are large enough to be considered anomalies.
• cv2.putText(): Displays a message (“Anomaly Detected”) when motion is detected.

This code serves as a basic example of motion detection for anomaly detection. For more advanced applications, learning-based methods or deep learning models can be integrated for greater accuracy.

Challenges and Considerations in Anomaly Detection

There are several challenges associated with anomaly detection:

1. Adaptation to Environmental Changes: Changes in lighting, weather, or camera positioning can lead to false detections. Robust models and adaptive algorithms are needed to handle these changes.
2. Defining Anomalies: The definition of “anomaly” varies by system, requiring customization to suit the specific context.
3. Privacy Concerns: Video analysis involves personal data, so it is crucial to ensure data protection and privacy when implementing these systems.

Summary

In this episode, we covered anomaly detection and how it is applied in surveillance systems to detect unusual behavior in real-time. We discussed both rule-based and learning-based methods, each with its own advantages and limitations. With advancements in deep learning, anomaly detection has become more accurate, enabling real-time surveillance monitoring.

Next Episode Preview

In the next episode, we’ll explore the challenges and future prospects of computer vision, focusing on current limitations and what lies ahead. How will AI and video analysis evolve, and what new possibilities will emerge? Stay tuned!

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Notes

• Autoencoder: A neural network trained to reproduce input data, often used for anomaly detection.
• LSTM (Long Short-Term Memory): A type of recurrent neural network used to analyze time-series data and detect patterns of behavior.