How AI cameras detect objects and recognize faces

Translator|Chen Jun

Reviser|Sun Shujuan

Artificial intelligence (AI) has been launched It's been around for decades, but only recently has the technology been widely used in scenarios such as assisting businesses in identifying potential customers and identifying dangerous objects in the environment. Especially in the field of object detection driven by artificial intelligence, it fundamentally improves the capabilities of traditional closed-circuit television (CCTV) surveillance cameras.

Currently, with the help of object recognition software, AI cameras can already recognize faces and various objects appearing in front of them. This has extremely practical and innovative significance for real security usage scenarios.

What is an AI camera?

First of all, let us clarify a concept: an AI camera is not a camera that can be used to shoot visual images or make videos. It is a new device, but a visual processing device that is very similar to a traditional camera and can use technologies such as computer vision to "learn" practical information from visual data.

Using machine learning algorithms, AI cameras can smoothly process various information in visual images. For example, one typical use is that AI cameras can use sensors to analyze images and determine the best settings to capture the image.

In recent years, object detection has been widely used in many vertical fields. For example, in some industries, some companies will rely on AI cameras for facial recognition, vehicle detection, and other semantic object detection.

In some special scenarios (such as construction sites), AI cameras can also promptly detect whether construction workers have worn basic safety protection equipment through safety protocols; or whether A high-altitude object is falling on the person's head.

In addition, by monitoring employee behavior, AI cameras can also determine whether employees are too close to hazardous materials while working and whether they are ignoring safety threat warnings. On the basis of this real-time danger detection, AI cameras can also use sound, light, electricity and other methods to remind on-site personnel of ongoing abnormal situations, or notify the background to save lives before an accident occurs and avoid costly error correction. cost.

How AI camera detects objects works

Object detection involves processing the image data captured by the camera through a certain algorithm and comparing it with the data in the database Known objects are compared. The algorithm then identifies objects that are similar to objects already in the database and returns the results. For example, AI cameras designed to detect faces can proactively identify people or other objects, even if some of their features are blocked or unrecognizable. The AI ​​camera compares the image it captures with a large amount of face information stored in a back-end database to retrieve facial features that may match.

At the same time, subject to explicit consent, these cameras can also enable employers to more effectively track employee attendance and monitor employees in the workplace through facial recognition technology. Behavior.

Training AI cameras to detect specific objects

Similar to other AI-powered tools, AI cameras must be trained on large data sets, such as Only after accepting the judgment of hundreds of thousands of car images can a specific vehicle be detected more effectively and accurately.

It can be seen that we first need to train the AI ​​camera to collect images of various objects to be detected. At this stage, we should achieve "Han Xin points out troops, the more the better", that is, displaying images including different viewing angles, lighting conditions, colors, and different shooting angles. Only by feeding the cameras richer images can they be able to repeatedly train their judgment capabilities. By continuously accumulating correct features and eliminating irrelevant interference factors, they can make accurate recognition in the real world.

In terms of implementation technology, you can use open source libraries such as TensorFlow Lite or PyTorch to train the algorithms you develop for AI camera systems to detect specific objects. The entire process includes writing code, calling algorithms to receive images or videos, and outputting tags corresponding to the content.

Advantages of using AI cameras for object detection

Although adding AI cameras will bring certain costs to enterprises, compared with the benefits it brings , many industries are still willing to accept and enable it. Below, I will take the D-Link series AI cameras as an example to discuss with you their four major advantages in real usage scenarios.

1. Faster detection time

Traditional camera systems tend to be slow and unreliable in detecting objects, and usually rely on human eye observation to accurately locate objects. AI cameras are designed and manufactured to detect objects quickly and accurately. With the rapid updates and iterations of today's AI technology, AI cameras have greatly shortened the detection time. This critical improvement is particularly important in fast-paced environments such as construction sites or public roads.

2. Higher accuracy

Compared with traditional camera systems, object detection cameras have also improved a lot in recognition accuracy. This is thanks in part to their ability to identify objects from multiple angles and distances. Even if objects appear to be similar in size or shape, the camera can tell the difference between them. Such characteristics make them more suitable for sophisticated application scenarios such as security monitoring and inventory management, and can also reflect the characteristics of artificial intelligence.

3. More cost-saving

Similarly, compared with traditional cameras, object detection cameras have higher accuracy and faster detection efficiency, which itself reflects Save time and cost. By investing upfront in building AI-enabled systems, companies can avoid costly mistakes and missed opportunities caused by inaccurate or slow results from traditional systems. Moreover, these systems tend to require less manual maintenance and even do not require regular manual calibration. Therefore, in the long run, AI cameras can indeed save companies’ capital investment.

4. Higher scalability

Due to the convenience of deployment and implementation, AI cameras can quickly realize monitoring capabilities without increasing the burden on resources. expansion and extension. Additionally, past manual identification methods required several operators to continuously stare at the screen to analyze and interpret what they saw in the image. The AI ​​camera provides more reliable results and avoids recognition errors that may occur when manual work is tedious.

Summary

In summary, artificial intelligence is playing a key role in various object detection fields by redefining traditional recognition and monitoring technology, and can even Have a life-saving effect. Of course, the actual application scenarios of AI technology are far more than this. From customer chatbots to content editing and popular AI painting, artificial intelligence continues to have a strong connection with our lives.

Translator Introduction

Julian Chen, 51CTO community editor, has more than ten years of experience in IT project implementation, and is good at implementing internal and external resources and risks Management and control, focusing on disseminating network and information security knowledge and experience.

Original title: ​​How AI Cameras Detect Objects and Recognize Faces​ ​, Author: KARIM AHMAD

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