Computer Vision Pipeline for Security and Safety¶

Use Case: Real-time Threat Detection in CCTV Footage¶

Problem: Real-time detection of potential threats (e.g., unauthorized access, suspicious behavior) in surveillance footage to ensure prompt security responses.

Pipeline Overview:¶

graph LR;
    A[Video Feed Acquisition] --> B[Preprocessing];
    B --> C[Object and Behavior Detection];
    C --> D[Threat Classification];
    D --> E[Alert System];
    E --> F[Data Logging & Reporting];

Description¶

Video Feed Acquisition: Continuous video capture from multiple CCTV cameras across monitored areas.
Preprocessing: Stabilize the video feed, denoise, and adjust for varying lighting conditions to ensure clear visibility of objects and individuals.
Object and Behavior Detection: Use a trained YOLO or Faster R-CNN model to detect objects (e.g., people, vehicles) and employ behavior analysis algorithms to recognize suspicious actions (e.g., loitering, trespassing).
Threat Classification: Classify detected objects and behaviors based on threat levels (e.g., high, medium, low) using a pre-trained deep learning model for anomaly detection.
Alert System: Generate real-time alerts for security personnel and activate automated responses (e.g., sounding alarms, locking doors) when high-threat behaviors are detected.
Data Logging & Reporting: Store identified threats with timestamps for incident reporting, investigation, and long-term analysis.

Implementation (Python) Real-time Threat Detection in CCTV Footage¶

This Python code detects suspicious behavior and triggers alerts based on threat classifications.

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# Load pre-trained models for object detection and threat classification
object_model = load_model('object_detection_model.h5')
threat_model = load_model('threat_classification_model.h5')

def preprocess_frame(frame):
    # Convert frame to grayscale and stabilize for processing
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    stabilized = cv2.GaussianBlur(gray, (5, 5), 0)
    return stabilized

def detect_objects(frame):
    # Use object detection model to find objects and coordinates
    processed_frame = preprocess_frame(frame)
    object_coords = object_model.predict(np.expand_dims(processed_frame, axis=0))
    return object_coords

def classify_threat(coords):
    # Classify threat level based on detected object and behavior
    threat_prediction = threat_model.predict(np.expand_dims(coords, axis=0))
    if threat_prediction > 0.8:
        return "High Threat"
    elif threat_prediction > 0.5:
        return "Medium Threat"
    return "Low Threat"

def monitor_security(camera_id=0):
    cap = cv2.VideoCapture(camera_id)
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        object_coords = detect_objects(frame)
        threat_level = classify_threat(object_coords)
        if threat_level == "High Threat":
            print("Alert! High threat detected!")
        elif threat_level == "Medium Threat":
            print("Warning! Medium threat detected.")

        cv2.imshow('Real-time Threat Detection', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

monitor_security()

Outputs¶

This pipeline enables real-time monitoring for security and safety applications, allowing for rapid response and threat mitigation in critical scenarios.

References¶

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