Lu Neng. Fuzzy Multi-Objective Optimization Model of Basketball Lineup. Dynamic Systems and Applications 29 (2020) No. 5, 1968 – 1978

https://doi.org/10.46719/dsa202029530

ABSTRACT.
Provided development of a computer vision system to monitor basketball players from indoor team games. Some of the image processing and monitoring methods, including calibration and lens warpage correction data sets are listed. Selecting a sports team within a limited budget is a complex task that is considered to be limited multipurpose optimization and multi-criterion decision-making. The use of objective methods for the player of choice increase the likelihood of selection as a function of variables and performance indicators necessary for the level of each game, it will be considered to be increasingly necessary. In a Fuzzy Multi-Objective Optimization Model (FM-OOM)model based on image edge detection technology, grayscale processing, object capture, target recognition, etc. combine with the actual needs of the data to achieve the various requirements for in-game image detection. The key feature of these improvements is basketball detection and monitoring. A deletion study can be performed and then a strong tracker can be created with features, i.e. without the need to extract situations such as proximity or color similarity, or without the use of a FM-OOM. The tracker provided includes the following: (1) according to a detection, it uses a FM-OOM to evaluate players’ appearance, followed by (2) a tracking, which controls the pose and semantic information from the output.

Keywords: Fuzzy Multi-Objective Optimization Model, Detection and Tracking, Basket Ball.