Pose estimation is a computer vision task that involves estimating the position and orientation of an object or person in an image or video. It usually involves identifying specific key points or body parts (such as joints) and determining their relative position and orientation. Pose estimation has many applications, including robotics, augmented reality, human-computer interaction, and motion analysis.
Top-down and bottom-up are two commonly used methods in pose estimation. The main difference between top-down and bottom-up pose estimation methods is the order in which poses are estimated.
In a top-down approach , an object detection model is used to identify objects of interest, such as people or cars, and a separate pose estimation model is used to estimate the object's keypoints.
In contrast, bottom-up approaches first identify individual body parts or joints and then connect them to form a complete pose.
To summarize, top-down methods first detect objects and then estimate their poses, while bottom-up methods first identify body parts and then form complete poses.
Implementation model
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