Online, simultaneous shot boundary detection and key frame extraction for sports videos using rank tracing

2008 ◽  
Author(s):  
Wael Abd-Almageed
Keyword(s):  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Shot Boundary ◽  
Sports Videos

Key Frame Extraction from Video

2014 ◽  
Vol 4 (2) ◽  
pp. 68-79 ◽  
Author(s):  
Sergii Mashtalir ◽  
Olena Mikhnova
Keyword(s):  
Artificial Intelligence ◽  
Shot Boundary Detection ◽  
Post Processing ◽  
Extraction Techniques ◽  
Key Frame Extraction ◽  
Estimation Techniques ◽  
Key Frame ◽  
Shot Boundary ◽  
Textual Features ◽  
Key Frames

A complete overview of key frame extraction techniques has been provided. It has been found out that such techniques usually have three phases, namely shot boundary detection as a pre-processing phase, main phase of key frame detection, where visual, structural, audio and textual features are extracted from each frame, then processed and analyzed with artificial intelligence methods, and the last post-processing phase lies in removal of duplicates if they occur in the resulting sequence of key frames. Estimation techniques and available test video collections have been also observed. At the end, conclusions concerning drawbacks of the examined procedure and basic tendencies of its development have been marked.

A Novel Algorithm for Key Frames Selection

2012 ◽  
Vol 182-183 ◽  
pp. 2025-2029
Author(s):  
Chen Kuei Yang ◽  
Shyi Chyi Cheng
Keyword(s):  
Boundary Detection ◽  
Experimental Results ◽  
Shot Boundary Detection ◽  
Color Model ◽  
Frame Selection ◽  
Key Frame ◽  
Shot Boundary ◽  
Key Frame Selection ◽  
Key Frames ◽  
Novel Algorithm

The main objective of this study framework is to help the user to management the interesting video images from the huge storage device. The study can be divided into three main steps is listing as follows: shot boundary detection, key frame selection and evaluation method are designed by analyzing the video content. In this paper we proposed a novel algorithm for shot boundary detection. First, transform the RGB color model into LUV color model according our experimental results. The useful features of each frame are extracted from sequence images by the automatically computation of their color moment-preserving and matching 64-predefined bit planes in order to detect the shot boundary. The excellent shot boundary detection will result in the good key frames selection. Second, the key frame selection is proceed by applying the geometric invariant moments which successful be used in target tracing, pattern recognition, and facial recognition. In our experiments we used five test sequences that belong to five different program categories: movies, advertising, sport game, news, and commercial documentary. The experimental results showed that our method is feasible, effective, and is better than the former approaches.

An Efficient Shot Boundary Detection Using Data-cube Searching Technique

2020 ◽  
Vol 13 (4) ◽  
pp. 798-807
Author(s):  
J. Kavitha ◽  
P. Arockia Jansi Rani ◽  
P. Mohamed Fathimal ◽  
Asha Paul
Keyword(s):  
Texture Features ◽  
Boundary Detection ◽  
Data Cube ◽  
Multimedia Data ◽  
Shot Boundary Detection ◽  
Gradual Transition ◽  
Detection Rates ◽  
Effective Manner ◽  
Shot Boundary ◽  
Shot Transitions

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

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