Video Sequence Analysis for On-Table Tennis Player Ranking and Analysis

With the rapidly developing of the scientific research in the field of sports, big data analytics and information science are used to carry out technical and tactical statistical analysis of competition or training videos. The table tennis is a skill oriented sport. The technique and tactics in table tennis are the core factors to win the game. With the endlessly emerging innovative playing techniques and tactics, the players have their own competition styles. According to the competition events among athletes, the athletes’ competition relationship network is constructed and the players’ ranking is established. The ranking can be used to help table tennis players improve daily training and understand their ability. In this paper, the table tennis players’ ranking is established their competition videos and their prestige scores in the table tennis players’ competition relationship network.

The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism

With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.

Non-contact heart rate assessment based on spectral analysis of video images

Heart rate is one of the main physiological indicators of the body, and the parameters of heart rate variability reflect various aspects of the functional and psychoemotional status. Automatic determination of a person's condition based on video sequence analysis is an important problem in various areas related to ensuring the safety of production, air and transport communications, prevention of crimes and terrorist threats, etc. Therefore, an important task is to develop methods and algorithms for analyzing video images that allow remote monitoring of heart rate parameters. Purpose – development and software implementation of a method for non-contact assessment of heart rate based on spectral analysis of a video image of a person's face recorded using a traditional video camera. A method for non-contact measurement of heart rate has been developed, including the stages of data collection and preprocessing, spectral analysis of video images and analysis of the information obtained to calculate the results. The difference between the values of the average heart rate recorded using contact sensors and using the developed software does not exceed 3-4 beats per minute. The proposed approach can be implemented as part of various information systems where it is required to control the functional and psycho-emotional status of a person, for example, systems for operator’s status monitoring.

A Survey of Human Activity Interpretation in Image and Video Sequence

In the past, a large amount of intensive research has been dedicated to the interpretation of human activity in image and video sequence. This popularity is largely due to the emergence of the wide applications of video cameras in surveillance. In image and video sequence analysis, human activity detection and recognition is critically important. By detecting and understanding the human activity, we can fulfill many surveillance related applications including city centre monitoring, consumer behavior analysis, etc. Generally speaking, human activity interpretation in image and video sequence depends on the following stages: human motion detection and human motion interpretation. In this chapter, the authors provide a comprehensive review of the recent advance of all these stages. Various methods for each issue are discussed to examine the state of the art. Finally, some research challenges, possible applications, and future directions are discussed.