A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video IEEE...

A Novel Framework for Semantic Annotation andPersonalized Retrieval of Sports Video

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 10, NO. 3, APRIL 2008

Outline

Introduction Semantic Annotation of Sports Video

Text Analysis Video Analysis Text/Video Alignment Video Annotation and Indexing

Personalized Video Retrieval Experiment and Evaluation

Text Analysis

Caption text overlaid on the video The recognition of caption text overlaid on sports

video using OCR is not ideal due to the quality of the broadcast sports video.

Closed caption Closed caption is a transcript from speech to text

thus contains a lot of information irrelevant to the games and lacks of a well-defined structure.

Text Analysis

Web-casting text is another text source related to sports video It is available in many sports websites such as BBC and

ESPN and can be easily accessed during or after the game

The content of web-casting text is more focused on events of sports games and has a well-defined structure

Since webcasting text is a text counterpart of broadcast sports video, it includes detailed information of an event in sports games

The analysis of web-casting text

ROI Segmentation Keyword Identification Text Event Detection

ROI Segmentation

Keyword Identification

Text Event Detection

Example 1 (soccer):

79:19 Goal by Didier Drogba (Chelsea) drilled left-footed from right side of six-yard box (6 yards). Chelsea 4-1 Bayern Munich

Example 2 (basketball):

8:52 Kobe Bryant makes 17-foot two point shot (Smush Parker assists). LA Lakers 9-11 Denver

The presentation style of the event for soccer and basketball in web-casting text is slightly different, but the event and event semantics can be easily extracted and represented using a common structure as follows.

Goal by Frank Lampard of Chelsea at 58:58 (soccer)

Video Analysis

Shot Classification Replay Detection Video Event Modeling

Event with replay

far view shot, close-up shots, replay, close-up shots, far view shot

Text/Video Alignment Event Moment Detection

Clock Digits Location

Clock Digits Recognition

Event Boundary Detection Hidden Markov Model (HMM)

Video Annotation and Indexing For each game, we annotate the video in two le

vels L1 ： annotation exhibits an overall game summary

including game name, date, place, teams, number of audience, scores, etc

L2 ： annotates each event in the video using text semantics extracted from the text event and video boundaries obtained from text/video alignment<Event> <Priority> by <Player> of <Team> at <Time> <VideoStartFrame> <VideoEndFrame>

Video Annotation and Indexing

The precisions and recalls of all the events except precision of the shot event for soccer (97.1%) achieve 100%.

Shot Classification and Replay Detection

Evaluation on Personalized Retrieval

A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video IEEE...

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