Intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video

Babaguchi, Noboru; Nitta, Naoko

doi:10.1109/icip.2003.1246886

Cited by 22 publications

(32 citation statements)

References 14 publications

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“…Event detection is approached either by developing feature-based event models [8], [12], [13], [22]- [24], [26], by searching for keywords in speech (e.g. commentator) [4] and closed captions [16], by using MPEG-7 metadata [11] or by involving several of the above-mentioned clues into inter-modal collaboration [3], [9], [21]. We see the main disadvantage of this approach in the need for numerous and reliable event models which should take into account not only all highlight-related events but also various realizations of these events and their coverage that may change from one broadcaster to another.…”

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confidence: 99%

Adaptive extraction of highlights from a sport video based on excitement modeling

Hanjalic

2005

IEEE Trans. Multimedia

119

114

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Abstract-This paper addresses the challenge of automatically extracting the highlights from sports TV broadcasts. In particular, we are interested in finding a generic method of highlights extraction, which does not require the development of models for the events that are thought to be interpreted by the users as highlights. Instead, we search for highlights in those video segments that are expected to excite the users most. It is namely realistic to assume that a highlighting event induces a steady increase in a user's excitement, as compared to other, less interesting events. We mimic the expected variations in a user's excitement by observing the temporal behavior of selected audiovisual low-level features and the editing scheme of a video. Relations between this noncontent information and the evoked excitement are drawn partly from psychophysiological research and partly from analyzing the live-video directing practice. The expected variations in a user's excitement are represented by the excitement time curve, which is, subsequently, filtered in an adaptive way to extract the highlights in the prespecified total length and in view of the preferences regarding the highlights strength: extraction can namely be performed with variable sensitivity to capture few "strong" highlights or more "less strong" ones. We evaluate and discuss the performance of our method on the case study of soccer TV broadcasts.Index Terms-Affective video content analysis, video abstraction, video content modeling, video content pruning, video highlights extraction.

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confidence: 99%

Adaptive extraction of highlights from a sport video based on excitement modeling

Hanjalic

2005

IEEE Trans. Multimedia

119

114

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“…Some of the works mentioned above [12,18,20,24,25] , and also [7,15,[26][27][28][29] combined features from different modalities, and reported better concept inference as compared to using just single modality analysis.…”

Section: Related Workmentioning

confidence: 99%

“…For techniques relying on embedded or external text [6,8,10,26,27], the main issue is availability. In [6,26] for instance, CCs are not available in many countries, so their utilization, even though valuable, would be limited.…”

Section: Text-based Analysismentioning

confidence: 99%

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Overlaid Text Recognition for Matching Soccer-Concept Keywords

Halin

Rajeswari

Ramachandram

2008

2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation

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Overlaid-text appears frequently in broadcast sports video. They provide a plethora of information regarding the goings-on of a particular game. Examples include important events and video segments of interest such as bookings and half-time analysis, respectively. Furthermore, it is common that overlaidtext is displayed when a particular concept is happening or has happened. This paper presents a concept identification framework, based on matched keywords from overlaid-text extraction and recognition. Possible occurrences of overlaid-text in soccer programs are extracted and recognized, and then matched against a soccer-term database. Preliminary experiments show reliable character extraction, whose recognition has been successfully matched with keywords within the database.

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“…Previous works have illustrated that inter-modal collaboration based on multi-modal streams (e.g. visual and text [32], audio and motion [33]) can improve the robustness of the system. Hence, we have also applied the available information from different modalities for our setup to create a multi-level, multi-modal system.…”

Section: System Overviewmentioning

confidence: 99%

Automatic composition of broadcast sports video

Wang

Chng

et al. 2008

Multimedia Systems

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This study examines an automatic broadcast soccer video composition system. The research is important as the ability to automatically compose broadcast sports video will not only improve broadcast video generation efficiency, but also provides the possibility to customize sports video broadcasting. We present a novel approach to the two major issues required in the system's implementation, specifically the camera view selection/switching module and the automatic replay generation module. In our implementation, we use multi-modal framework to perform video content analysis, event and event boundary detection from the raw unedited main/sub-camera captures. This framework explores the possible cues using mid-level representations to bridge the gap between low-level features and high-level semantics. The video content analysis results are utilized for camera view selection/switching in the generated video composition, and the event detection results and mid-level representations are used to generate replays which are automatically inserted into the broadcast soccer video. Our experimental results are promising and found to be comparable to those generated by broadcast professionals.

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Intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video

Cited by 22 publications

References 14 publications

Adaptive extraction of highlights from a sport video based on excitement modeling

Adaptive extraction of highlights from a sport video based on excitement modeling

Overlaid Text Recognition for Matching Soccer-Concept Keywords

Automatic composition of broadcast sports video

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