What is object recognition? How does it work?

The capabilities of AI and machine learning have already gone beyond human minds. There are some embedded features of modern AI solutions like analyzing data, making accurate predictions, finding invisible patterns, object recognition, etc.

The most exciting feature of AI is object recognition which will play a key role in shaping the future of AI with humans.

Let's dive deeper and understand object recognition and how it works.

What is object recognition?

Object recognition is an AI-based computer vision technique for identifying objects in images or videos with the guidance of specified patterns. Object recognition is the key output of deep learning and machine learning.

Object recognition aims to teach machines to recognize people, objects, scenes, and other visual details just like humans do.

The best example of object recognition is the facial recognition feature of smartphones, where the authorized owner sets a screen lock that opens only when the owner shows his face to the mobile camera to unlock the smartphone.

What are the use cases of object recognition?

Object recognition is a revolutionary step in the growth of AI and machine learning. There are several uses of object recognition. 

Below are some use cases of object recognition.

Security and Surveillance:

Object recognition is very effective, and useful when it comes to security and surveillance. Private enterprises use objects, faces, and retina recognition technologies embedded in smart cameras for public safety.

Automotive Industry:

AI has grown manifolds that can beat humans in many things. All the things that look impossible a few decades ago are getting possible now the driverless car is an example of it. The technology is recognizing objects that come their way and then makes decisions depending on the environment. 

Retail:

The cameras in the store with visual object recognition features can detect customers and track their behaviour in the store. It helps retailers understand consumers' behaviour and sell what they actually want. 

How do object recognition work in machine learning and deep learning?

There are different ways to recognize objects with images and videos. Here are the latest approaches used for object recognition with images, but the difference is with execution. The two approaches are machine learning and deep learning.

Object recognition with machine learning

The way modern technology is evolving, the task of detecting objects has simplified a lot with modern algorithms. Machine learning uses a collection of images and brings the most relevant image for you. The machine learning algorithm extracts edges or corners of all the photos and then differentiates between classes in your data. 

Object recognition with deep learning

Deep learning is quite advanced in terms of object recognition. Deep learning uses Convolutional Neural Networks or CNNs, which automatically learn inherent features of objects to identify the object.

Object recognition techniques 

Object recognition algorithms use different techniques to identify, detect, and recognize objects. The OpenCV library allows you to use the following tactics to make the algorithm work and learn.

Shape-based Recognition: The most common technique to identify an object is to figure out its shape, match that with the collection, and get the best out.

Colour-based Matching: It is very much similar to shape-based recognition. In this technique, the algorithm differentiates the objects based on their colours. For example, if algorithms have to differentiate oranges from apples, they will keep the red colour fruit on one side and the red colour fruit on the other side.

Template Matching: It is the elementary-level technique that separates objects based on characters, letters, numbers, and objects.

Active-passive Recognition: Passive recognition means algorithms identify or recognize the images from existing collections. On the other hand, active recognition implies providing algorithms with live images to allow them to conclude.

Conclusion

Using Artificial Intelligence, Machine Learning, and Deep Learning is a promising tool for custom software development. The future lies with Artificial Intelligence and Machine Learning. With time, the use cases of technology in every sector will rise significantly, and object recognition will be benefited from it as well.