Automatic Event Detection in User-Generated Video Content: A Survey

2021 ◽  
pp. 279-287
Author(s):  
Alamuru Susmitha ◽  
Sanjay Jain ◽  
Mihir Narayan Mohnaty
Keyword(s):  
Event Detection ◽  
Video Content ◽  
Automatic Event Detection

OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica

2021 ◽  
Author(s):  
Leonardo van der Laat ◽  
Ronald J.L. Baldares ◽  
Esteban J. Chaves ◽  
Esteban Meneses
Keyword(s):  
Deep Learning ◽  
Costa Rica ◽  
Event Detection ◽  
Seismic Monitoring ◽  
Automatic Event Detection

Temporal-Based Video Event Detection and Retrieval

2012 ◽  
Vol 3 (4) ◽  
pp. 39-51
Author(s):  
Min Chen
Keyword(s):  
Event Detection ◽  
Video Retrieval ◽  
Class Imbalance ◽  
Video Data ◽  
Temporal Information ◽  
Temporal Association ◽  
Association Mining ◽  
Video Content ◽  
Video Content Analysis ◽  
High Level

The fast proliferation of video data archives has increased the need for automatic video content analysis and semantic video retrieval. Since temporal information is critical in conveying video content, in this chapter, an effective temporal-based event detection framework is proposed to support high-level video indexing and retrieval. The core is a temporal association mining process that systematically captures characteristic temporal patterns to help identify and define interesting events. This framework effectively tackles the challenges caused by loose video structure and class imbalance issues. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The temporal information and event detection results can then be input into our proposed distributed video retrieval system to support the high-level semantic querying, selective video browsing and event-based video retrieval.

Temporal-Based Video Event Detection and Retrieval

2011 ◽  
pp. 214-227
Author(s):  
Min Chen
Keyword(s):  
Event Detection ◽  
Video Retrieval ◽  
Class Imbalance ◽  
Video Data ◽  
Temporal Information ◽  
Temporal Association ◽  
Association Mining ◽  
Video Content ◽  
Data Archives ◽  
High Level

The fast proliferation of video data archives has increased the need for automatic video content analysis and semantic video retrieval. Since temporal information is critical in conveying video content, in this chapter, an effective temporal-based event detection framework is proposed to support high-level video indexing and retrieval. The core is a temporal association mining process that systematically captures characteristic temporal patterns to help identify and define interesting events. This framework effectively tackles the challenges caused by loose video structure and class imbalance issues. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The temporal information and event detection results can then be input into our proposed distributed video retrieval system to support the high-level semantic querying, selective video browsing and event-based video retrieval.

scholarly journals Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

2015 ◽  
Vol 21 (S3) ◽  
pp. 2329-2330
Author(s):  
Jeremy R. Teuton ◽  
Richard L. Griswold ◽  
B. Layla Mehdi ◽  
Nigel D.Browning
Keyword(s):  
Event Detection ◽  
Compression Algorithm ◽  
Algorithm Analysis ◽  
Automatic Event Detection
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