Abstract
The adaptive object model method is an effective way to develop dynamic and configurable adaptive software. It has the characteristics of metamodel, description drive, and runtime reflection. First, the core idea of the adaptive object model is explained; then, the five modes of establishing the metamodel in the adaptive object model architecture, the model engine, and supporting tools are analyzed; and the basketball tracking algorithm of the adaptive object model is discussed. Secondly, a two-dimensional joint information strategy is proposed to improve the tracking effect. When the basketball is in a very complex environment, there will always be some color information in the background that is the same as the target, which affects the effect of basketball tracking. Therefore, this paper proposes a Camshift tracking method based on the significance of histograms, through real time. The basketball movement is compared with the background histogram to continuously adjust the basketball movement tracking model. These two methods can better establish the tracking model of the basketball adaptive object, reduce the interference of background information, and achieve the effect of stable tracking of the target. The simulation experiment results show that the method proposed in this paper can effectively improve the accuracy of the basketball goal model and achieve stable tracking of the goal.
Highlights
Adaptive object model is a hot research topic in knowledge engineering
It is a model for describing and defining the model. It is the first abstraction of the objects in the problem domain at a higher level of the model to form a model described by classes; after the second-time abstract [1, 2], forming a metamodel described by metaclass, AOM uses metadata to describe the configuration information of a new or modified system and interprets and executes the object model established with metadata [3] at runtime. e interpretation of the model is to use runtime reflection technology to map metadata to the runtime description of the object model
For the algorithm in this article, compared with the traditional Camshift algorithm, the process of statistical background histogram is added, and no other auxiliary features are extracted. e target model obtained by this method increases the time consumption, but the increased time consumption is not very large
Summary
Adaptive object model is a hot research topic in knowledge engineering. It is a kind of knowledge representation model. An intelligent control system that uses the adaptive object model is to combine object model method basketball motion tracking technology and fuzzy theory. E user model describes the individual characteristics of the user, such as the description of the basic information of the learner (name, gender, date of birth, phone number, e-mail, education level, and occupation), learning style, cognitive level and hobbies, learning behavior records, and the learning history of the learner (e.g., the type of media that the learner accesses to learning resources, learning time, and the number of visits); the system can continuously update the user model according to the user’s learning history. Global navigation is mainly presented by the domain knowledge tree structure. e tree structure can display the complete knowledge system of the course, and the learning status mark shows the current learner’s knowledge state of mastery
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