From autonomous driving to facial recognition, object classification is a part of our everyday lives. Here’s a look at how these advancements in computer vision are assisting both companies and individuals.
We live in an increasingly visual age. Today’s technological advancements make the task of distinguishing what’s real and what’s fake more difficult than ever before. We’ve heard news stories about deepfakes for years, and advancements in AI have made this sort of mimicry more accessible than ever.
Thankfully, computer vision can make deciphering between real and synthetic material easier for all. It also makes everyday necessities—like facial recognition and spatial mapping for autonomous vehicles—a reality.
Computer vision allows machines to accomplish a variety of tasks that were once only capable with the human brain. Computers can now detect and categorize objects within image and video content. These two tasks are getting better with time, and sometimes, computer vision can detect objects better than people can.
In this blog, we’ll explore the application of computer vision in image and object classification. We’ll also explore how it’s solving real-world problems.
What is Object Detection and Classification?
First, let’s look at a few definitions and differences between these technologies.
- Object detection finds objects within an image or video.
- Object classification determines which specific objects are within an image or video actually are. It labels these objects.
- Object localization specifically tracks where objects are located in an image or video. This determines the position of any object within a piece of visual content.
What Are Common Uses of Object Classification and Detection?
Facial recognition is one of the most common uses of computer vision processing information, detecting a very specific object, and classifying it. With the help of machine learning, computers can detect and recognize faces with remarkable accuracy. Many of us have this enabled on our own cell phones. This technology helps security systems identify potential intruders or anomalies quickly and accurately. It provides a high level of safety for businesses and people alike.
We see this technology applied in a variety of areas beyond facial recognition as well. It’s critical to advancements in cancer detection, surveillance systems, and autonomous driving. For example, self-driving cars rely on computer vision algorithms to detect, identify, and track objects in real time. In this application, computer vision tracks many things, such as:
- Other vehicles
- Traffic signals
- Lane markings
A car that drives autonomously must recognize these objects accurately and quickly in order to make decisions about where and how to move. Computer vision detects road conditions and adjusts the car’s speed accordingly. Object classification also understands each individual environment and the behaviors of other people using the road, ultimately helping the car take appropriate actions.
Shutterstock.AI’s Object Classification for Amazon Web Services
Recently, Amazon Web Services (AWS) needed a large volume of high quality visual content to train models for Amazon Rekognition. Rekognition is a digital service that helps companies add image and video analyses to applications, without needing an on-staff expert in machine learning. AWS used Shutterstock’s asset library of over 400 million diverse images, videos, and 3D models to train their computer vision models on.
AWS used Shutterstock.AI’s data to improve object detection for a wide variety of driving scenarios and road conditions. It ensured that vehicles with AI sensors operate safely around objects and people, detect traffic in 360 degrees, create 3D maps of spaces, and ultimately navigate roads without a driver controlling every movement of a car.
But Amazon Web Services is not alone. More and more companies are turning to Shutterstock for AI-based solutions, especially for high-quality data to train computer vision models on. To learn more about Shutterstock’s work with computer vision, download our white paper. It examines the growth of computer vision applications across industries, current legal discussions, and ethical concerns. It also presents a four-point framework for creating trustworthy computer vision and ensuring maximum benefits for organizations and individuals alike.
License this cover image via Neo Geometric, AN NGUYEN, and Vladimir Sukhachev.