典型文献
Shot classification and replay detection for sports video summarization
文献摘要：
Automated analysis of sports video summarization is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective video summarization framework based on shot classification and replay detection for field sports videos. Accurate shot classification is mandatory to better structure the input video for further processing, i.e., key events or replay detection. Therefore, we present a lightweight convolutional neural network based method for shot classification. Then we analyze each shot for replay detection and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the extreme learning machine for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for shot classification and replay detection to summarize fi eld sports videos.
文献关键词：
中图分类号：
作者姓名：
Ali JAVED;Amen ALI KHAN
作者机构：
Department of Software Engineering,University of Engineering and Technology,Taxila 47050,Pakistan
文献出处：
引用格式：
[1]Ali JAVED;Amen ALI KHAN-.Shot classification and replay detection for sports video summarization)[J].信息与电子工程前沿（英文）,2022(05):790-800
A类：
octa,broadcasters,baseball,cricket
B类：
Shot,classification,replay,detection,sports,summarization,Automated,analysis,challenging,due,variations,cameras,speed,illumination,conditions,editing,effects,game,structure,genre,etc,To,address,these,challenges,effective,framework,shot,field,videos,Accurate,mandatory,better,input,further,processing,key,events,Therefore,lightweight,convolutional,neural,network,method,Then,analyze,each,specifically,successive,batch,logo,frames,that,identify,segments,from,For,this,purpose,local,pattern,features,represent,train,extreme,learning,machine,proposed,robust,designs,placement,transitions,performance,our,evaluated,dataset,containing,diverse,YouTube,soccer,Experimental,results,demonstrate,can,reliably,used,summarize
AB值：
0.429695
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