Sport action mining: Dribbling recognition in soccer

Abstract

Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players' positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players' locations. The proposed method takes advantage of an association rule mining algorithm (e.g., FPGrowth) to generate displacement sequences for modelling actions in soccer. In this context, transactions are sequences of traces representing player displacements, while itemsets are players' coordinates on the pitch. The experimental results pointed out the Random Forest classifier achieved a balanced accuracy value of 93.3% for detecting dribbling actions, which are considered complex events in soccer. Additionally, the proposed framework provides insights on players' skills and player's roles based on a small amount of positional data.

Application of Sports Video Image Analysis Based on Fuzzy Support Vector Machine

“…The location and size of the players and the course can be marked. In the detection of table tennis, the Gaussian distribution weight is selected to make the central position of the Dalian Tong district more prominent [17]. This is the correct position of the ping-pong ball.…”

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye. These systems are able to evaluate the activities of players successfully [79] such as the distance covered by players, shot detection [80,81], the number of sprints, player's position, and their movements [82,83], the player's relative position concerning other players, possession [84] of the soccer ball and motion/gesture recognition of the referee [85], predicting player trajectories for shot situations [86]. The generated data can be used to evaluate individual player performance, occlusion handling [21] by the detecting position of the player [87], action recognition [88], predicting and classifying the passes [89][90][91], key event extraction [92][93][94][95][96][97][98][99][100][101], tactical performance of the team [102][103][104][105][106], and analyzing the team's tactics based on the team formation [107][108][109], along with generating highlights [110][111][112][113].…”

“…Due to the size, speed, velocity, and unstructured motion of the ball compared to the players and playfield in various sports, it is still an open issue to detect and track the ball. Various AI algorithms have been developed to achieve better performance in various sports such as soccer [62,84], basketball [33,38], tennis [148], and badminton [195,196] in terms of detecting and tracking concerning various aspects of the ball.…”

Modeling the formation of defensive gaps in basketball: Cutting on a teammate’s drive

“…Many sport analytical models concentrate on event detection using e.g., Hidden Markov Models in basketball [ 1 ] or ensemble tree modeling in soccer [ 2 ]. Even the classification of more complex tactical elements was successfully implemented years ago [ 3 , 4 ], and the expansion of deep learning creates more insights about the value of the different actions happening on the field [ 5 ].…”