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Understand the principle of machine learning, deep learning and neural networks
The use of machine learning, deep learning and neural network techniques in the field of computer vision and video analysis has revolutionized many sectors, from security to medicine to industrial automation. These technological advances have allowed machines to “see” and “understand” their environment in ways similar to, or even superior to, human ability. Deep learning is a branch of machine learning, which itself is a subset of artificial intelligence.
Machine learning involves training algorithms using data so that they can perform specific tasks, such as recognizing objects, shapes, or even actions in video sequences. It is a field of artificial intelligence (AI) that focuses on developing techniques that allow computers to learn and improve from experience, i.e. from data which are provided to him.
The process can be summarized according to the following steps:
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Collection of data relevant to the task in question.
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Cleaning, transforming and preparing data for analysis.
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Using data to train a mathematical model, which is an algorithm that can find patterns and relationships in data.
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Adjusting model parameters to minimize the error between its predictions and the actual values of the training data.
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Evaluating the model with validation data to ensure it generalizes well beyond the training data.
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Using the validated model in production to make predictions on new data.
(Netflix, Canal+ Series and Apple TV+ use machine learning to recommend films and series to their users.)
Deep learning, on the other hand, is a sub-discipline of machine learning that uses deep artificial neural networks to solve complex problems in perception and analysis of visual data. The term "deep" in deep learning refers to the fact that these neural networks typically have many layers (hence the name "deep neural networks"), which allows them to learn hierarchical features from the data, similar to how the human brain works.
Deep learning first involves feeding a computer a large number of examples of a particular task, such as recognizing faces in photos. Then, the algorithm gradually adjusts its own settings to become more and more adept at accomplishing this task. Once training is complete, the computer can automatically spot faces in the new photos based on what it has learned.
Deep learning has significantly improved the accuracy of face detection compared to traditional methods. It is capable of processing a wide variety of lighting conditions, viewing angles and facial expressions, making it an essential technology for applications such as security, facial recognition for device unlocking and classification of photos on social media.
(Example of face detection in a photo: Usinenouvelle.com)
Neural networks, inspired by the functioning of the human brain, are particularly effective at extracting information from images and videos. Neural networks are a fundamental component of deep learning and machine learning. These are mathematical structures that are designed to mimic (in a very simplified way) the functioning of neurons in the human brain. A neural network is made up of layers of interconnected neurons, each with weight and activation functions. Neural networks are used to capture complex patterns in data and perform tasks such as classification, regression, text generation, etc.
(Simplified diagram of neural networks which illustrate the interconnection between the different successive layers. Becoz.org)
Neural networks play an essential role in speech recognition used by voice assistants.
(Google Home)
In conclusion, the integration of machine learning, deep learning, and neural networks into computer vision and video analytics has revolutionized many industries, providing perception and analysis capabilities comparable or even superior to those of man. Applications such as security, medicine and industrial automation benefit greatly. It is in this context that VERA intervenes. VERA is a module dedicated to running AI models, such as neural networks, for video analysis. Using VERA's capabilities, one can automate object detection, monitor complex environments in real time, and analyze video streams to make informed decisions.
(VERA front view)
However, these advances also raise concerns, particularly around privacy, mass surveillance and algorithmic bias.
Openness to the future lies in continued research and the establishment of appropriate regulations to guarantee ethical and responsible use of these technologies.Computer vision and AI in video have immense potential to improve our daily lives, but striking a balance between technological innovation and the protection of individual rights is essential to avoid the pitfalls and controversies associated with these advances.
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