What process would you use to separate humans from other objects in a photo?

The process for separating humans from other objects in a photo is best represented by detection. In the context of computer vision, detection involves identifying and localizing specific objects within an image, such as people, animals, vehicles, etc. This is typically done by drawing bounding boxes around the detected objects, which helps distinguish humans from other elements in the photo.

Detection algorithms, such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector), are commonly used for this purpose. They analyze the entire image to identify the presence and position of objects in real time.

While segmentation is also related to distinguishing objects within an image, it goes a step further by pixel-wise classification, basically outlining the exact shape of the objects. In contrast, verification usually deals with confirming the identity or authenticity of an object rather than distinguishing it from others in an image. Classification, on the other hand, assigns a label to whole images rather than distinguishing specific objects within them. Therefore, detection is the most appropriate choice for the task of identifying and separating humans from other objects in a photo.