In the evolving landscape of corporate security, a quiet revolution is underway. Daulet Yermanov, an expert in artificial intelligence and machine learning, explores whether a single skilled developer, armed with the right tools, can transform a company’s security infrastructure.
This article examines a new approach to AI integration in corporate environments, offering a glimpse into a future where agility and innovation outperform scale and resources.
Today a paradigm shift can be witnessed. The integration of computer vision and machine learning into security systems isn’t just a technological upgrade—it’s a complete reimagining of the approach to corporate safety.
This transformation doesn’t necessitate vast teams or bottomless budgets. Today’s tools and libraries are so sophisticated that a single experienced developer can create and implement systems that would have required an entire department just a few years ago.
For instance, a facial recognition system can be implemented in a single day—far faster than traditional corporate solutions, which often take months or even years.
Traditional video surveillance systems, despite automation, still require human involvement and offer limited security improvements.
A study by IPVM, a key player in the security industry, revealed that only 1% of recorded footage is ever viewed in real-time by security personnel. Increasing staff doesn’t address this gap.
AI-based security systems, in contrast, offer numerous advantages. Neural networks allow these systems to operate autonomously, analyzing 100% of recorded video without human intervention.
They can predict potential security incidents and alert employees to react proactively. Trained with the right data sets, these systems can detect important details even in challenging conditions, such as low light or complex environments.
Today, it’s possible to implement such an AI system in just 24 hours, maintained by a single developer, at a fraction of the cost of traditional CCTV surveillance.
A robust ecosystem of libraries in AI, computer vision, and machine learning now exists, allowing developers to build sophisticated neural network-based video surveillance with minimal effort.
These tools, such as OpenCV, Dlib, and face_recognition, are open-source and highly customizable, meaning companies can tailor them to their specific security needs without incurring additional costs.
- OpenCV, for example, offers more than 2,500 general-purpose tools for AI-based security and computer vision, all accessible through a few lines of code. It is cross-platform, fast, and supported by an active developer community, making implementation straightforward.
- Dlib, while also cross-platform, offers more advanced machine learning features. It supports various operating systems and languages, from C++ to Python.
- face_recognition provides simpler but effective facial recognition functionality, ideal for companies seeking basic security features without complex customization.
The power of these tools is amplified when combined. A single developer can leverage these libraries to quickly implement a comprehensive security system that previously required significant resources.
Key Security Functionality in A Few Lines of Code
Here are some examples of how one developer is solving the most pressing video surveillance problems using open-source computer vision libraries. Using OpenCV, you can easily recognize people’s faces in a video. Connect the library:
import cv2Then, download ready-made .xml datasets for machine learning that contain properties that can be used to recognize faces in the camera image:
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')Then, take the image from the camera image:
camera = cv2.VideoCapture(0)
ret, img = camera.read()And in any loop, in this case the script is written in Python, find faces on the image using the methods face_cascade.detectMultiScale() and eye_cascade.detectMultiScale() by passing them the appropriate arguments.
Dlib takes this further by identifying 68 unique facial landmarks, which can be used to recognize individuals. Once the system has captured a face, the neural network can match it against a known set of facial characteristics:
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")The face_recognition methods allow you to perform the simplest face identification manipulations in just three lines:
import face_recognition
image = face_recognition.load_image_file("person.jpg")
face_locations = face_recognition.face_locations(sample)Unfortunately, the library does not have the same fine-tuning as OpenCV and although it is simpler, it can only replace OpenCV if a company implements absolutely typical functionality without any complexity.
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