Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video

Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.

Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

From Repression to Attrition

The Umbrella Movement displayed an unprecedented spectacle of resistance and brought Hong Kong under worldwide attention. This chapter demonstrates how the city’s hybrid regime adaptively switched its response to this protest from repression to attrition, after police repression catalyzed heightened mobilization. This strategy of attrition entailed an array of defensive and offensive tactics that extended beyond ignoring protests. By switching to this strategy, the regime actively sought to maintain elite cohesion and block political opportunities while leveraging countermovements and legal interventions to increase the participation cost of the protests and mobilize public discontent. Despite resistance from protesters, the success of attrition suggests its potential use by hybrid regimes as an effective regime response and a flexible holder of tactics.

Notational analysis of UEFA Europa League match Malmö FF - Chelsea FC

The purpose of using notation analysis is to collect as much data as possible regarding the abilities and characteristics of individual players, as well as the entire team. Using the collected database, coaches can make objective decisions about the way individual players and the entire team play. The paper analyzes a match between FF Malmӧ and FC Chelsea, played in the UEFA Europa League round of 32. The overall score was 1:2 for Chelsea. A notational scouting analysis was conducted using a programme called Pinnacle Studio 15. Offensive actions were analyzed, from the moment the team came into possession of the ball. Player activities were recorded by means of the notational system. A total of 30 elements of offensive tactics were monitored. The possession of the ball (66/34), the total and the totally correct significantly higher number of passes (781/321; 741/265), the shot on goal inside the penalty area (4/1) and the higher percentage of successful dribbles in the game 1:1 (68, 75/50) can be singled out as significant factors in the end result in favor of FC Chelsea. All that has been analyzed indicates the way FF Malmö plays, as well as the way in which one should stand against such a play, which is the primary goal and reason for conducting the notational analysis.

Opponents' Size Influences Maternal Aggression

20 female Long-Evans rats were tested for aggression on the day they gave birth. Only 30% of females tested with male intruders appreciably larger than themselves bit their opponents, and none showed offensive behaviors. By contrast, 80% of females tested with smaller males bit the intruders, and all of these used male-like offensive tactics. All attacked intruders exhibited defensive behavior, and all but one emitted ultrasonic vocalizations usually associated with pain. None of the males retaliated against the females. It is suggested that the size of the larger males evokes fear and that fear reduces offensive tendencies.