A multi-temporal framework for high-level activity analysis: Violent event detection in visual surveillance

2018 ◽  
Vol 447 ◽  
pp. 83-103 ◽  
Author(s):  
Donghui Song ◽  
Chansu Kim ◽  
Sung-Kee Park
Keyword(s):  
Event Detection ◽  
Activity Analysis ◽  
Visual Surveillance ◽  
Violent Event ◽  
Multi Temporal ◽  
High Level

Audio self organized units for high-level event detection

2013 ◽  
Author(s):  
Xiaodan Zhuang ◽  
Shuang Wu ◽  
Pradeep Natarajan ◽  
Rohit Prasad ◽  
Prem Natarajan
Keyword(s):  
Event Detection ◽  
Self Organized ◽  
High Level

Event Detection in Sports Video Based on Generative-Discriminative Models

2011 ◽  
pp. 143-165
Author(s):  
Guoliang Fan ◽  
Yi Ding
Keyword(s):  
Event Detection ◽  
Semantic Analysis ◽  
Building Blocks ◽  
Semantic Space ◽  
Sports Video ◽  
Low Level ◽  
Discriminative Models ◽  
Video Mining ◽  
High Level ◽  
Different Levels

Semantic event detection is an active and interesting research topic in the field of video mining. The major challenge is the semantic gap between low-level features and high-level semantics. In this chapter, we will advance a new sports video mining framework where a hybrid generative-discriminative approach is used for event detection. Specifically, we propose a three-layer semantic space by which event detection is converted into two inter-related statistical inference procedures that involve semantic analysis at different levels. The first is to infer the mid-level semantic structures from the low-level visual features via generative models, which can serve as building blocks of high-level semantic analysis. The second is to detect high-level semantics from mid-level semantic structures using discriminative models, which are of direct interests to users. In this framework we can explicitly represent and detect semantics at different levels. The use of generative and discriminative approaches in two different stages is proved to be effective and appropriate for event detection in sports video. The experimental results from a set of American football video data demonstrate that the proposed framework offers promising results compared with traditional approaches.

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.

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