AI (Artificial Intelligence) technologies have been starting to be pervasive/ubiquitous in various situations of the real world, and are also pervasive in sports. However, such AI technologies as Computer Vision and IoT (Internet of Things) in sports are expensive in cost for many high-spec cameras and sensors, and could be applied to only the games in sports that make sufficient profit and recover costs, e.g., the games of UEFA EURO 2024™ with high potential as businesses in sports/entertainment. For other games that do not make sufficient profit and recover costs, AI technologies need to be reasonable, i.e., affordable, easy to install, and capable of performing modestly. Therefore, this paper first develops a specific model for using Ultralytics YOLOv8, that is a SOTA (State-Of-The-Art) object detection, for analysis of sports play video shot only by a single camera in Kin-Ball sport. Note that Kin-Ball sport stands out as an alternative sport, but it is not such a major sport that can make sufficient profit and recover costs. Second, this paper proposes and validates novel methods to track the Bounding Boxes of in-video objects (e.g., a ball, players, and referees) based on linear interpolation and three kinds of vector interpolations. The results object-tracked by YOLOv8 and ByteTrack for a Kin-Ball sports play video might lose a part of trajectories and Bounding Boxes of in-video objects, but the proposed methods try to interpolate the lost part(s).