Body posture recognition

下面为大家整理一篇优秀的assignment代写范文- Body posture recognition，供大家参考学习，这篇论文讨论了人体姿态识别。随着信息技术的发展和智能科技的普及，如今人体姿态识别技术已开始在计算机视觉相关领域中广泛应用。人体姿态识别主要在于研究描述人体姿态以及预测人体行为，其识别过程是指，在指定图像或视屏中，根据人体中关节点位置的变化，识别人体动作的过程。人体姿态识别的算法主要分为深度图和RGB图像。无论是深度图技术还是RGB图像技术，都是通过计算机强大的运算能力进行人体姿态的动作预算，通过这样的方式能够一定程度地实现人工图像的监测，并且能够为人工智能的普及奠定良好的基础。

Human posture recognition mainly focuses on the study and description of human posture and prediction of human behavior. Its recognition process refers to the process of recognizing human movements according to the changes of the position of the key points in the human body in the designated image or video screen. There are two kinds of algorithms for body attitude recognition. One is based on depth graph, and the other is directly based on RGB image. Depth maps are images taken by the camera, with each pixel representing the distance of the object from the XY plane of the camera. The application of this algorithm is easily restricted by acquisition equipment requirements, but based on RGB image algorithm directly through the red, green, blue three color channel change and the superposition between each other to get the color identification, not is limited by the interference of other factors, thus has more development prospect, and also made a lot of progress. At present, even in relatively complex and certain fixed scenes, the human posture estimation algorithm based on RGB image can achieve good recognition effect compared with the human posture estimation algorithm based on depth map. Both depth map technology and RGB image technology use the powerful computing power of computers to budget the motion of human posture. In this way, artificial image monitoring can be realized to a certain extent and a good foundation can be laid for the popularization of artificial intelligence. With the continuous improvement of China's social level, people's requirements for the quality of social life are also increasing. Therefore, video monitoring has become an indispensable security measure for people in the actual life process, and the technical requirements based on video analysis are getting higher and higher. For example, it has been widely used in the fields of intelligent home decoration, medical treatment and motion analysis, etc., and the role of body posture recognition in solid state scenarios in various fields is obvious. Especially in recent years, China's security work has been strengthened, which has a strong demand for the dense population flow in big cities and the screening of criminals.

Human posture is mainly divided into recognition based on computer perspective and recognition based on motion capture technology. The recognition based on computer vision mainly USES various feature information to identify human posture and movements, such as video image sequence, human contour, multi-perspective, etc. The recognition based on computer vision can easily obtain the track and contour information of human motion, but there is no way to specifically express the details of human motion, and it is easy to identify errors due to occlusion and other problems. Human posture recognition based on motion capture technology is to identify human motion track by locating human node and storing its motion data. Compared with the human posture recognition from the computer perspective, the human posture recognition based on motion capture technology can better reflect the human posture information, better process and record the motion details, and will not affect the recognition of the motion track because of the object color or occlusion. The innovation of technology has a strong auxiliary effect on the analysis and capture of human posture, and can better show the details of the movement, which has a high reference value for the professionals to carry out the trace management of the motion analysis. Through a good sports prospect budget, reasonable prediction can be made in various calculation methods, and the adaptability in various environments can also be enhanced to a certain extent. Due to the direction of the future monitoring implementation is in all the fields of video monitoring, so for the user's specific requirements should also be timely to technological innovation, the user demand for technology is the development direction of technical innovation, mainly in the process of the algorithm improve, should also strengthen the trajectory adopt method of ascension.

The body attitude recognition in fixed scene is realized step by step through three steps: foreground extraction, feature vector extraction and attitude classification recognition. Firstly, features are extracted from the foreground image, and geometric features such as position, direction, circumference, aspect ratio, area and enclosed area ratio and centrifugation rate are calculated in the foreground image. Then, feature vectors formed by extracted geometric feature parameters reflect human posture. Finally, according to the specific requirements of the algorithm, different human posture images are collected as samples, and the features are extracted as data sets, which are used as data sources for reference, so as to achieve the purpose of human posture recognition. The whole data, from data collection to data sorting to data calculation, relies on the powerful computing ability of computers and the rationality of mathematical modeling. Therefore, to enhance the accuracy of human posture recognition system, it depends on the computing ability of computers and the scientificity of mathematical modeling to some extent. To achieve these, systematic training of professionals should be strengthened, and mathematical modeling should be done with reason. Mathematical modeling of motion foreground should also include background modeling of scene. The whole modeling process is integrated. Therefore, background modeling as a module in mathematical modeling engineering has a strong impact on the accuracy of human posture prediction. For environment modeling for the science of human body posture prediction accuracy is the good guarantee, because the environment is constantly changing as time changes, the background will be affected by light, wind speed, and other natural factors, therefore, in the background, the attitude of the human body simulation, you need to these natural factors, considering the mathematical modeling, as much as possible and update model, which will be more objective reflect the natural phenomenon, to ensure the accuracy of the prediction.

The acquisition of attitude characteristic parameters is another important task to ensure the posture recognition of human body in fixed scene, and it is also an important link to affect the accuracy of prediction results. The choice of characteristic parameters have certain experience, choose the right characteristic parameter to describe the content, to maximise the accuracy of the prediction, and has strong guiding significance for the classification process, when designing the recognition system can refine the system as much as possible, realize preprocessing, model selection, parameter selection and classification criterion and so on several big modules. A more detailed preprocessing of the system can realize the accuracy of feature parameter selection and play a connecting role in the prediction, which is the key step to ensure the accuracy of the whole system. Therefore, the selection of attitude characteristic parameters is of great significance to the model composition of the whole system.

Body posture recognition has a wide range of applications, which can be used in human-computer interaction, film and television production, motion analysis, game entertainment and other fields. People can use human posture recognition to locate the motion track of human body joints and record their motion data, and realize 3D animation to simulate human motion to make movies and television. Motion can also be analyzed through recorded orbits and data; It can also realize man-machine interaction and game entertainment. For example, motion sensing game is to realize game interaction by identifying human motion posture.

At present, the most extensive application of human posture recognition is in intelligent monitoring. The difference between intelligent monitoring and general monitoring mainly lies in embedding human posture recognition technology into video server, and using algorithms to identify and judge the behavior of dynamic objects -- pedestrians and vehicles -- in the monitoring screen scene, extract the key information, and timely alert users when abnormal behavior occurs. Similarly, the human posture recognition technology under fixed scenarios can be applied to family monitoring. For example, in order to prevent falls of elderly people living alone, intelligent monitoring equipment that recognizes falls of elderly people living alone can be installed at home to identify falls of elderly people living alone and respond to emergencies in a timely manner. The continuous development of human society and the improving of the quality of life, video monitoring has been very widely used in various fields, people's life continuously expand and extend of the space, public and private places also in development, meet the probability of all kinds of emergency is growing, especially in public places, because of its monitoring is difficult, densely populated. Simple monitoring can no longer meet the requirements of today's social development. Simply relying on the staff on duty, it is difficult to predict human posture, which is also a potential waste of social resources. Therefore, choose intelligent monitoring system has become the current society, the only way to solve this fundamental problem in the process of social and human in addition to the language outside the body movements can also pass a certain information, computer prediction can be achieved through a more scientific and reasonable action interpretation of the meaning of and implementing social help people better.

There are still some challenges in the practical application of body posture recognition in fixed scenes. For example, it is difficult to recognize a variety of similar movements, and human limbs are flexible and complex. It is difficult to recognize human posture when there are frequent exchanges or similar body movements. In addition, changes in clothing, changes in perspective and other visual reasons have also caused serious impact on the body posture recognition and great difficulties. There is still a long way to go to improve the accuracy prediction of posture recognition technology in fixed scenes. Of course, mathematical modeling is also an inevitable choice for its development. As more and more people choose plastic surgery in today's society, it also brings more challenges to human posture recognition to some extent.

With the continuous improvement of science and technology in China and the continuous improvement of computer graphics algorithm, the research in the field of artificial intelligence will continue to deepen. The cultivation of high-new-type artificial intelligence talents has become an inevitable trend in the development of social education. Therefore, it has become an important direction for the development of human posture recognition under fixed scenes in the future to extend the existing simple posture in a more scientific and reasonable way and conduct further research. Under the premise of using information technology, visual processing can be done more efficiently, so as to describe more complex actions, which will be an important direction for the development of the industry in the future.

At present, human posture recognition in fixed scenes is still in a state of development, and there are still many large and small defects, facing challenges and pressures from different aspects, but the achievements it can achieve are just around the corner. The research achievements of the intelligent monitoring system based on gesture recognition has been recognized by people, such not only can help customers more quickly and find the monitoring key, and the ability to achieve resource integration, targeted to extract the effective information, but also can save manpower material resources, has brought great convenience to people's production and life.

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