Computer vision is a branch of artificial intelligence that enables machines to see. Applications of computer vision are extremely widespread, and this field is also growing at an astonishing rate. According to the research firm MarketsandMarkets, artificial intelligence in the visual analytics market is expected to reach $3 billion by 2021. There are various advantages of using computer vision algorithms for analyzing images. Computer vision algorithms can detect and recognize objects in images, analyze images for statistical properties, and segment different areas of an image based on their unique features or characteristics. In this blog post, we will discuss some practical applications of computer vision techniques in crowd analysis with examples.

What is Crowd Analysis?

Crowd analysis is the process of collecting data and information about the people in a crowd and their activities. This can be done by collecting data from a variety of sources, but one of the most common sources is a video feed. Crowd analysis can be used for a variety of reasons, but most commonly it is used to monitor public events and emergencies. While it might seem like a relatively simple task to collect data about people in a crowd, there are actually quite a few challenges that must be overcome. First, a video feed can potentially contain a large amount of data, so selecting what data to collect and how to collect it can be quite a challenge. Furthermore, the data may be collected from a variety of sources, so it must be standardized and integrated together. Finally, the data collected must be stored and analyzed so that it can be used to make decisions in real time.

Computer Vision Techniques that Help in Crowd Analysis

Computer vision techniques can be used to help in the analysis of crowds. These include face detection, object detection and recognition, image scoring and classification, and more. Let’s take a look at each of these in turn. Face detection: Face detection is the process of finding faces in an image. It is a prerequisite to many other computer vision tasks. Once you know where the faces are in an image, you can then use other computer vision techniques to identify the people in those faces. Face recognition: Face recognition is the process of identifying a particular person from a collection of faces. This can be done by either comparing the faces to a database of images or by generating a unique identifier for the face. Object detection and recognition: Object detection is the process of finding objects in an image. Object recognition is the process of identifying the objects in an image. Image scoring and classification: Image scoring and classification is the process of assigning a score or a label to an image based on the image itself.

Conclusion

Crowd analysis is the process of collecting data and information about the people in a crowd and their activities. Computer vision techniques can be used to help in the analysis of crowds. These include face detection, object detection and recognition, image scoring and classification, and more. Computer vision is a branch of artificial intelligence that enables machines to see. Applications of computer vision are extremely widespread, and this field is also growing at an astonishing rate. According to the research firm MarketsandMarkets, artificial intelligence in the visual analytics market is expected to reach $3 billion by 2021.