High-Level Codewords Based on Granger Causality for Video Event Detection

Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis. In this paper, we propose a new framework to perceive high-level codewords by analyzing temporal relationship between different channels of video features. The low-level vocabulary words are firstly generated after different audio and visual feature extraction. A weighted undirected graph is constructed by exploring the Granger Causality between low-level words. Then, a greedy agglomerative graph-partitioning method is used to discover low-level word groups which have similar temporal pattern. The high-level codebooks representation is obtained by quantification of low-level words groups. Finally, multiple kernel learning, combined with our high-level codewords, is used to detect the video event. Extensive experimental results show that the proposed method achieves preferable results in video event detection.

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Temporal-Based Video Event Detection and Retrieval

International Journal of Organizational and Collective Intelligence ◽

10.4018/ijoci.2012100103 ◽

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.

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RECENT ADVANCES IN VIDEO CONTENT ANALYSIS: FROM VISUAL FEATURES TO SEMANTIC VIDEO SEGMENTS

International Journal of Image and Graphics ◽

10.1142/s0219467801000062 ◽

2001 ◽

Vol 01 (01) ◽

pp. 63-81 ◽

Cited By ~ 6

Author(s):

ALAN HANJALIC ◽

REGINALD L. LAGENDIJK ◽

JAN BIEMOND

Keyword(s):

Temporal Structure ◽

Situation Comedy ◽

Video Content ◽

Visual Stream ◽

Video Content Analysis ◽

Low Level ◽

Situation Comedies ◽

News Program ◽

The One ◽

High Level

This paper addresses the problem of automatically partitioning a video into semantic segments using visual low-level features only. Semantic segments may be understood as building content blocks of a video with a clear sequential content structure. Examples are reports in a news program, episodes in a movie, scenes of a situation comedy or topic segments of a documentary. In some video genres like news programs or documentaries, the usage of different media (visual, audio, speech, text) may be beneficial or is even unavoidable for reliably detecting the boundaries between semantic segments. In many other genres, however, the pay-off in using different media for the purpose of high-level segmentation is not high. On the one hand, relating the audio, speech or text to the semantic temporal structure of video content is generally very difficult. This is especially so in "acting" video genres like movies and situation comedies. On the other hand, the information contained in the visual stream of these video genres often seems to provide the major clue about the position of semantic segments boundaries. Partitioning a video into semantic segments can be performed by measuring the coherence of the content along neighboring video shots of a sequence. The segment boundaries are then found at places (e.g., shot boundaries) where the values of content coherence are sufficiently low. On the basis of two state-of-the-art techniques for content coherence modeling, we illustrate in this paper the current possibilities for detecting the boundaries of semantic segments using visual low-level features only.

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