An adaptive anchor frame detection algorithm based on background detection for news video analysis

2016 ◽  
Author(s):  
Ruilin Xu ◽  
Chun-Yu Tsai ◽  
John R. Kender
Keyword(s):  
Video Analysis ◽  
Detection Algorithm ◽  
News Video

AUTOMATIC DETECTION AND VERIFICATION OF TEXT REGIONS IN NEWS VIDEO FRAMES

2002 ◽  
Vol 16 (02) ◽  
pp. 257-271 ◽  
Author(s):  
JIANMING HU ◽  
JIE XI ◽  
LIDE WU
Keyword(s):  
Detection Algorithm ◽  
Difficult Problem ◽  
Edge Image ◽  
Textual Information ◽  
Run Length ◽  
Video Frames ◽  
News Video ◽  
Length Analysis ◽  
Text Location ◽  
Automatic Text

Textual information in a video is very useful for video indexing and retrieving. Detecting text blocks in video frames is the first important procedure for extracting the textual information. Automatic text location is a very difficult problem due to the large variety of character styles and the complex backgrounds. In this paper, we describe the various steps of the proposed text detection algorithm. First, the gray scale edges are detected and smoothed horizontally. Second, the edge image is binarized, and run length analysis is applied to find candidate text blocks. Finally, each detected block is verified by an improved logical level technique (ILLT). Experiments show this method is not sensitive to color/texture changes of the characters, and can be used to detect text lines in news videos effectively.

scholarly journals Video analysis method of basketball training assistant based on deep learning theory during COVID-19 spread

2020 ◽  
Vol 39 (6) ◽  
pp. 8747-8755
Author(s):  
Wan Guochen ◽  
Shan Feihong
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Video Analysis ◽  
Ant Colony Algorithm ◽  
Detection Algorithm ◽  
Image Correlation ◽  
Free Throw ◽  
Tactical Decision ◽  
Reliability And Robustness ◽  
Frame Set

During covid-19, basketball training was stopped. Instead, the basketball video analysis is used. In this paper, literature, theoretical analysis, numerical simulation, experimental research and other research methods are used. The ant colony algorithm model of deep learning optimization for basketball technical and tactical decision-making is established to solve the optimization problem of actual technical and tactical decision-making. In this paper, video image correlation algorithm is used. In the video of players’ free throw basket, there are many independent frames. The real frame set of free throw basket includes the whole process of jumping, arm lifting, squatting and stretching. The shooting frame set and shooting information of the ball are obtained. In this paper, a shot frame detection algorithm is proposed by analyzing multiple samples of multi shot video. The mathematical model of the shooting frame is established, which can locate the shooting frame quickly and accurately and determine the penalty frame set. Further obtain the basketball release status information for preparation. The reliability and robustness of the algorithm are verified by experiments on several samples. It provides a new method for basketball training during covid-19.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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