Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.

Hybrid Method of Video Shot Segmentation Based on YCbCr Space Color Model

In this digital world, Video analysis is the most important and useful task. Currently, tremendous tasks have been done in video analysis like compressing the videos, video retrieval process and video database indexing, etc. For all these tasks, one common step is segmenting the video shots, which are referred to as Video Shots Segmentation (VSS). Video shots segmentation is used to segment the input videos into a number of frames sequentially where the scene changes occurred, i.e. called shots. In this article, segmenting the video shots follows a hybrid procedure. Here, we have introduced the moments of colors, distance metrics and threshold techniques. All the videos follow the above mentioned steps for segmenting the video shots. But, before that, the input video is converted into a specific color model i.e. YCbCr. Then, apply the color moments to extract the feature vectors of frames, which are differentiated based on the color features of frames. In every two frames of the video, distance metrics methods are applying to compute the similarity and dissimilarity of frames. And the dissimilarity of the frames can be computed by using the threshold technique to get the shots from the video. In this paper, we are using the adaptive threshold technique to segment the videos into various shots. In this step, we will get a true number of shots. By the experimental results, this proposed methodology can be evaluated with the sequence of videos based on the performance or evaluation metrics.

Video Equipment and Video Shot Composition

This 6-page publication about video equipment and video shot composition is part of a series on developing effective video production practices. This series also covers video production, scriptwriting, and video editing. Minor revision by Ricky Telg and Peyton Beattie; published by the UF/IFAS Department of Agricultural Education and Communication.

Video Editing

This 2-page publication about video editing is part of a series about developing effective video production practices. This series also covers video production, scriptwriting, and video equipment and video shot composition. Minor revision by Ricky Telg and Peyton Beattie; published by the UF/IFAS Department of Agricultural Education and Communication.

An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

Shot boundary detection in videos is one of the most fundamental tasks towards content-based video retrieval and analysis. In this aspect, an efficient approach to detect abrupt and gradual transition in videos is presented. The proposed method detects the shot boundaries in videos by extracting block-based mean probability binary weight (MPBW) histogram from the normalized Kirsch magnitude frames as an amalgamation of local and global features. Abrupt transitions in videos are detected by utilizing the distance measure between consecutive MPBW histograms and employing an adaptive threshold. In the subsequent step, co-efficient of mean deviation and variance statistical measure is applied on MPBW histograms to detect gradual transitions in the video. Experiments were conducted on TRECVID 2001 and 2007 datasets to analyse and validate the proposed method. Experimental result shows significant improvement of the proposed SBD approach over some of the state-of-the-art algorithms in terms of recall, precision, and F1-score.

An Analysis of Cognitive Elements and Effects of Video Commercials

This chapter starts with an analysis of differences between the indexes attached to still images in the image database and cognitive elements of still images extracted from the cognitive experiment. It then analyzes the relationship between cognitive elements of each video shot generated based on still images and cognition of the video commercial produced by combining them. Lastly, it discusses how to index the video shot and production methods of video commercials based on the analyses. The image database tends to attach the records of the timing and place of shooting and features of the persons as indexes. In order to produce an attractive video commercial, it is necessary that video shots convey rich cognitive elements that are not too simple. This ensures that when combining several video shots, their cognitive elements have some consistency and consistent cognitive elements are woven into the video commercial to constitute a rich semantic network.