Visual human pose recognition algorithm is a research hotspot in the field of computer vision in recent years. With the rise of deep learning algorithms, visual human pose recognition algorithms have been greatly improved in recognition accuracy and efficiency. At present, the application scenarios of visual human pose recognition algorithms are becoming more and more extensive, including but not limited to games, virtual reality, sports training, human-computer interaction, medical rehabilitation and other fields.
In addition to the visual human pose recognition algorithm, there are also the following human pose recognition algorithms:
1. Sensor recognition algorithm: human posture data is obtained through sensors (such as accelerometers, gyroscopes, etc.), and then human posture information is obtained through data analysis and algorithm processing. Its advantages are high precision and sensitivity, suitable for indoor, static, local range and other scenarios, but it requires contact operation, not suitable for dynamic and large-range scenes.
2. Attitude capture system algorithm: use multiple cameras or sensors and other devices to capture and recognize human posture, and combine 3D modeling technology to reconstruct human models and perform pose analysis. The advantage is that more accurate attitude reconstruction and analysis can be performed, but it requires more equipment and space, and the cost is higher.
3. Algorithm based on motion capture: through the monitoring and capture of human motion state, the human posture is inferred and analyzed. The advantage is that it can perform fine motion capture and analysis, but it requires multiple iterations of experiments to train the model, which is more expensive.
Compared with other gesture recognition algorithms, the visual human pose recognition algorithm has the advantages of non-contact, strong real-time, wide applicability, and wide data sources, and is suitable for a variety of different application scenarios and has better universality and scalability. At the same time, the visual human pose recognition algorithm adopts deep learning model for training and optimization, which has high accuracy and recognition accuracy, which can effectively avoid misjudgment and missed judgment, so it has relatively great advantages in the current field of pose recognition.
Compared with other pose recognition algorithms, the visual human pose recognition algorithm has the following advantages:
1. Non-contact: The visual human pose recognition algorithm does not need to contact the monitored object, and can observe the object through the camera, avoiding interference and influence on the object.
2. Real-time: The visual human pose recognition algorithm can quickly and accurately identify and analyze the human posture in the real-time video stream, with high real-time and real-time feedback capabilities.
3. High precision: The visual human pose recognition algorithm uses deep learning models for training and optimization, which has high accuracy and recognition accuracy, which can effectively avoid misjudgment and missed judgment.
4. Strong applicability: The visual human pose recognition algorithm can be applied to a variety of different application scenarios, such as human-computer interaction, sports analysis, medical rehabilitation, virtual reality and other fields, with strong applicability and scalability.
5. Wide range of data sources: visual human pose recognition algorithms can use existing image and video datasets for training, such as large-scale human pose datasets, video surveillance datasets, etc., with a wide range of data sources and a large amount of data, which can better ensure the accuracy and robustness of the algorithm.
In summary, the visual human pose recognition algorithm has the advantages of high precision, strong real-time and wide applicability, and has gradually become one of the mainstream algorithms in the field of human pose recognition.