Computing in memory refers to the fusion of storage and computing resources to improve computational efficiency and data processing speed. Human recognition is a computer vision technology based on images or videos, aimed at recognizing and classifying human form, movement, facial expressions, and other characteristics.
Specifically, computing in memory architecture can play a role in the human recognition algorithm:
· Feature extraction: Human recognition algorithms usually require feature extraction from a large amount of image or video data, which requires a lot of computing resources and storage space. By using computing in memory technology, seamless data exchange between storage and computing can be achieved, thereby quickly and efficiently completing the feature extraction process.
· Data storage: Human recognition algorithms require a large amount of data for training and testing, which usually needs to be stored in high-capacity storage devices. By using computing in memory technology, storage and computing resources can be tightly combined, achieving faster and more efficient data storage and management.
· Parallel computing: Human recognition algorithms usually require a large amount of data computation, which requires a lot of computing resources. By using computing in memory technology, parallel computing can be achieved, thereby accelerating the speed and efficiency of the algorithm.
· Intelligent decision-making: Human recognition algorithms can use computing in memory technology to achieve intelligent decision-making output. By combining storage and computing, human data can be better processed and analyzed, thereby achieving more accurate and intelligent decision-making output.
Computing in memory technology has a wide range of applications in human recognition, which can improve the speed and efficiency of algorithms and provide better technical support for intelligent security and smart city fields. Human recognition algorithms can be used in smart door locks, specifically to achieve the following functions:
· Face recognition: By capturing a person's face image through a camera and using human recognition algorithms for analysis and matching, it can determine whether the door lock should be opened. This method is not only convenient and fast but also avoids problems such as key loss or password leakage.
· Motion detection: By analyzing the video image at the door, people's actions, such as knocking, clapping, or kicking the door, can be detected. This can remind homeowners that someone is visiting or prevent criminals from breaking the door lock.
· Facial expression recognition: By analyzing the video image at the door, people's facial expressions, such as smile, anger, or surprise, can be recognized. This can alert homeowners to the emotional state of people outside the door, helping to decide whether to open the door lock.
Human recognition algorithms can make smart door locks more convenient, secure, and intelligent, providing a better experience for people's daily lives.