Abstract
The development requirements for sports panorama synthesis technology, which rely on modern network technology, abandon traditional basketball training forms, and make effective use of the application and development of video panorama technology in the actual training process, are not only an important response to the current characteristics of students’ physical education learning and physical and mental growth but also an important response to the current characteristics of students’ physical education learning and physical and mental growth. In sports video analysis technology, a sports video panorama is a technical tool that converts an action video into a static action panorama to achieve the effect of action freezing and make overall analysis and mastery of the action easier. Shot segmentation is the foundation of hierarchical video structure, and it necessitates the accurate detection of all types of complex edited shot boundaries, as well as the effective distinction of motion changes in shots, in order to avoid shot boundary recognition being hampered. The synthesis technology for sports video panorama is investigated in this paper using edge computing and video shot boundary detection. After obtaining the boundary feature that describes the video shot, a comparison of this feature with a predetermined threshold value can be used to determine whether there is shot shear.
Highlights
In recent years, using the video images of athletes’ training and competition as the reference of sports training, it is an effective method to analyze sports at home and abroad [1]
This paper studies the sports video panorama synthesis technology based on video shot boundary detection
The gray histogram and the weighted block histogram were used as features to experiment on four videos in the section, and the detection effect and distribution of the recall evaluation algorithm were compared in three experiments, as shown in Figures 3 and 4
Summary
In recent years, using the video images of athletes’ training and competition as the reference of sports training, it is an effective method to analyze sports at home and abroad [1]. The basic principle of feature comparison method is to find enough image point coordinates from the same object in different perspectives in two consecutive frames and to obtain the global motion parameters by solving the superlinear equation [5]. The main motion and changes in the shot should be reflected in the key frame. Video retrieval relied primarily on users manually defining keywords for key images in the video and locating the segments they needed. Key frames are extracted from the segmented shots to represent high-level semantic and visual features such as scenes and stories, so as to make the video more concise [12]. The abrupt change detection of video shot is relatively simple, and there are many effective methods at present. The selection of the closed value plays a very important role in correctly detecting the abrupt change of the shot. For some extreme influence factors that cannot be eliminated even if insensitive features are adopted, such as the sudden change of instantaneous brightness with high intensity, some additional information is needed to assist the detection [15]
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