Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 07, May 2021

Basketball-51: A Video Dataset for Activity Recognition in the Basketball Game


Sarbagya Ratna Shakya, Chaoyang Zhang and Zhaoxian Zhou, University of Southern Mississippi, USA


In recent years, there has been an increase in the association of technology in sports and live sports broadcasting networks. From score updates, broadcasting commercials, assisting referees for decision making, and minimizing errors, the adoption of technology has been used for fair play and improve results. This has been possible with the advancement in video analysis, classification techniques, and the availability of resources. This paper introduces a new labelled video dataset collected from a live basketball game broadcasted on live TV to determine the type of basket scored in the basketball game. Among different shots, the points the player can score are basically of three types: 3 points, 2 points, which depends on the range of shots taken and 1 point which is the free shots taken after a foul. This dataset consists of labelled video clips collected from the live broadcast of the game from the broadcasting medium to classify different scoring activities. This paper also gives the preliminary analysis of the dataset for different class labels using 3D ConvNet and two-stream 3D ConvNet methods to show the complexity of the dataset.


Basketball dataset, 3D ConvNet, two-stream 3D ConvNet.