Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents

2016 ◽  
Vol 25 (12) ◽  
pp. 5689-5701 ◽  
Author(s):  
Litao Yu ◽  
Yang Yang ◽  
Zi Huang ◽  
Peng Wang ◽  
Jingkuan Song ◽  
...  
Keyword(s):  
Semantic Analysis ◽  
Event Recognition ◽  
Video Event ◽  
Web Video

scholarly journals Robust video event recognition

2021 ◽  
Author(s):  
Feifei Chen
Keyword(s):  
Semantic Analysis ◽  
Video Quality ◽  
Event Recognition ◽  
High Quality ◽  
Essential Information ◽  
Video Event ◽  
Generic Framework ◽  
Video Semantic Analysis ◽  
Similar Accuracy ◽  
Feature Extraction Technique

Video Event Recognition is an important problem in video semantic analysis. In Video Event Recognition, video scenes can be summarized into video event through understanding their contents. Previous research has proposed many solutions to solve this problem. However, so far, all of them only target high-quality videos. In this thesis, we find, with the constraints in modern applications, that low-quality videos also deserve our attention. Compared to previous works, this brings a greater challenge, as low-quality videos are lacking essential information for previous methods to work. With the quality degraded, technical assumptions made by previous works no longer stay intact. Thus, this thesis provides a solution to address this problem. Based on the generic framework proposed by previous work, we propose a novel feature extraction technique that can work well with low-quality videos. We also improve the sequence summary model based on previous work. As a result, comparing to previous works, our method reaches a similar accuracy but is tested with a much lower video quality.

scholarly journals Robust video event recognition

2021 ◽  
Author(s):  
Feifei Chen
Keyword(s):  
Semantic Analysis ◽  
Video Quality ◽  
Event Recognition ◽  
High Quality ◽  
Essential Information ◽  
Video Event ◽  
Generic Framework ◽  
Video Semantic Analysis ◽  
Similar Accuracy ◽  
Feature Extraction Technique

Video Event Recognition is an important problem in video semantic analysis. In Video Event Recognition, video scenes can be summarized into video event through understanding their contents. Previous research has proposed many solutions to solve this problem. However, so far, all of them only target high-quality videos. In this thesis, we find, with the constraints in modern applications, that low-quality videos also deserve our attention. Compared to previous works, this brings a greater challenge, as low-quality videos are lacking essential information for previous methods to work. With the quality degraded, technical assumptions made by previous works no longer stay intact. Thus, this thesis provides a solution to address this problem. Based on the generic framework proposed by previous work, we propose a novel feature extraction technique that can work well with low-quality videos. We also improve the sequence summary model based on previous work. As a result, comparing to previous works, our method reaches a similar accuracy but is tested with a much lower video quality.

A Fusion Approach Based ColorSiFT and Dense Trajectory Features for Video Event Recognition

2016 ◽  
Vol 14 (3) ◽  
pp. 81
Author(s):  
Nguyen Thanh Dieu ◽  
Tran Anh Tuan ◽  
Pham The Bao ◽  
Bui Nogc Nam ◽  
Jin Young Kim
Keyword(s):  
Event Recognition ◽  
Video Event ◽  
Dense Trajectory ◽  
Trajectory Features ◽  
Fusion Approach
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