Description: Human detection is the process of identifying and locating people in images or video streams. This field of computer vision relies on algorithms and machine learning models that analyze specific visual features to distinguish humans from other objects or elements in a scene. Human detection not only involves recognizing the presence of a person but also determining their position and orientation. This is achieved through techniques such as the use of convolutional neural networks (CNNs), which are particularly effective for image processing. The accuracy and speed of these systems have significantly improved in recent years, thanks to the availability of large datasets and advancements in computational power. Human detection is fundamental in various applications, from security and surveillance to human-computer interaction, and has become an essential component in the development of technologies such as autonomous vehicles and robotics. Its relevance lies in the ability of machines to interpret the environment similarly to humans, opening up a range of possibilities in automation and enhancing user experience.
History: Human detection has its roots in the early developments of computer vision in the 1960s, when basic methods for pattern recognition began to be explored. However, it was in the 2000s that significant advancements occurred, particularly with the introduction of deep learning algorithms. In 2014, the convolutional neural network known as R-CNN (Regions with CNN features) revolutionized the field by drastically improving accuracy in object detection, including humans. Since then, numerous variants and improvements have been developed, such as Fast R-CNN and YOLO (You Only Look Once), which have optimized both the speed and accuracy of detection.
Uses: Human detection is used in a variety of applications, including security and surveillance systems, where monitoring and detecting intruders is required. It is also applied in robotics, enabling robots to interact more effectively with humans. In the transportation sector, it is crucial for autonomous vehicles, which must identify pedestrians and other road users to ensure safety. Additionally, it is used in behavior analysis in various environments, helping businesses understand customer flow and optimize product placement.
Examples: An example of human detection is a security surveillance system that uses cameras equipped with detection algorithms to alert about the presence of people in restricted areas. Another case is the use of human detection technology in autonomous vehicles, such as those developed by various companies, which need to identify pedestrians and cyclists to avoid accidents. Additionally, in the field of robotics, service robots in various settings use human detection to interact with customers and provide assistance.
