Event based indexing of broadcasted sports video by intermodal collaboration

Babaguchi, Noboru; Kawai, Yoshihiro; Kitahashi, Tadahiro

doi:10.1109/6046.985555

Cited by 185 publications

(122 citation statements)

References 17 publications

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“…This type text always appeared during the whole video sequence, for example, the extracted local text lines of Fig The title text is usually provides more information on its corresponding highlight event inference than a long term text. They are purposively added during soccer video editing, and they are aimed at providing the audience some indicative information about the video content, such as a card or a goal [56], [62]. …”

Section: Title Text Detection Localization and Trackingmentioning

confidence: 99%

Semantic Based Sport Video Browsing

Qian¹

2012

Semantics in Action - Applications and Scenarios

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Section: Title Text Detection Localization and Trackingmentioning

confidence: 99%

Semantic Based Sport Video Browsing

Qian¹

2012

Semantics in Action - Applications and Scenarios

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“…We thus consider the video segmentation problem as a temporal video content trajectory breakpoint detection problem. 1 More precisely, it is a video polyline due to the discrete nature of the image frames. We first consider detecting breakpoints on a temporal video content trajectory using a Multi-Observation Hidden Markov Model (MOHMM) (Figure 4 (b)).…”

Section: Temporal Segmentation Of Surveillance Videosmentioning

confidence: 99%

“…Traditionally, a four-layer hierarchical structure is adopted for video structure analysis which consists of a frame layer, a shot layer, a scene layer and a video layer [1]. At the bottom of the structure, continuous image frames taken by a single camera are grouped into shots.…”

Section: Introductionmentioning

confidence: 99%

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Activity Based Video Content Trajectory Representation and Segmentation

Xiang¹,

Gong²

2004

Procedings of the British Machine Vision Conference 2004

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A novel approach is developed to segment continuous CCTV recordings according to the activities captured in the scene. This approach differs from previous approaches which are mostly based on shot change detection and shot grouping. Video content is represented by constructing a cumulative multi-event histogram over time. An on-line segmentation algorithm is then proposed to detect breakpoints in the video content, which is more robust to noise and computationally much more efficient compared to existing on-line segmentation algorithms. Its performance is compared to that of two off-line algorithms on surveillance videos monitoring an aircraft ramp area. Our experiments demonstrate that this activity based video content representation is superior to a colour histogram based representation for surveillance video segmentation.

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“…It is common to use the domain knowledge of the class of videos for processing them, such as [9] for baseball, [10] for American football, [11] for tennis, and [12,13] for cricket etc. We use the domain knowledge of the videos to build scene categories and approximate scene models.…”

Section: Visual Domain Processing Of Videosmentioning

confidence: 99%

Text Driven Temporal Segmentation of Cricket Videos

Sankar

Pandey

Jawahar

2006

Computer Vision, Graphics and Image Processing

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Abstract. In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional topdown information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.

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Event based indexing of broadcasted sports video by intermodal collaboration

Cited by 185 publications

References 17 publications

Semantic Based Sport Video Browsing

Semantic Based Sport Video Browsing

Activity Based Video Content Trajectory Representation and Segmentation

Text Driven Temporal Segmentation of Cricket Videos

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