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For example, if an underlying event is a goal event in a soccer recreation and the enter information to event detection system 100 is a video recording of a whole sport, multiple occurrences of the aim occasion could additionally be detected from the game recording. A collective of occasion occurrences is analyzed at act 350 by occasion characterization unit 240 to generate the characterization of the events detected from a knowledge stream. Such characterization may comprise numerous statistics concerning the occurrences of the occasion such as the distribution of the occurrences alongside time axis. Another instance could be the correlation between the event and the circumstances underneath which the event occurred. For instance, a objective occasion might occur under completely different conditions such as which player scored the aim. It could additionally be useful to compute the share of every player on a staff scoring a goal.
The new likelihood could also be extracted from characterization unit 240 and used to replace occasion models. Event detection unit 120 applies occasion models, which can be some or all of 220 a, 220 b, and 220 c, to determine events primarily based on given observations, which may be some or all of 210 a, 210 b, 210 c, and 210 d. The particulars about event detection unit 120 will be further discussed later in referring to FIGS.
Therefore, statement unit one hundred ten is said to occasion modeling unit a hundred thirty by amassing observations based mostly on event models. That is, the event models saved in event modeling unit a hundred thirty dictate both the remark assortment unit 110 and the event detection unit one hundred twenty. 1 is a excessive degree block diagram of an event detection system a hundred, which includes an remark collection unit 110, an event modeling unit 130, and an event detection unit a hundred and twenty. 1, remark collection unit 110 feeds related observations to occasion detection unit one hundred twenty. Event modeling unit a hundred thirty generates fashions for various events and shops the models so that they can be retrieved for occasion detection functions.
The relationships among unit one hundred ten, 120, and one hundred thirty are described in additional detail in FIG. 2, statement collection unit a hundred and ten generates a plurality of temporal statement collection 210 a, 210 b, 210 c, and 210 d. Event modeling unit one hundred thirty could comprise the occasion models at different ranges of abstraction. For example, the area knowledge 220 a, the context models 220 b, and the dynamic occasion models 220 c in FIG. 2 might kind a hierarchy of fashions for underlying occasions.
Different modalities may be built-in in several fashions. The exemplary embodiment of the current invention shown in FIG. 6 integrates statement streams from totally different modalities earlier than they are used for detection purposes. Such integration may be as easy as concatenating the observations from different data sources at any time instance to type a single remark vector. The method based on claim 1, whereby said multiple-layer models embody a high level domain-specific knowledge model and a dynamic hierarchical event mannequin. Event detected from different remark streams of the same modality may also be used to improve the general detection.
To use this mannequin to detect occasions, assortment unit a hundred and ten has to produce the observations needed to make detection selections at various tree nodes. Event characterizations may also be used for other purposes. 11 reveals the exemplary relationship between occasion characterization unit 240 and occasion model adaptation unit 250.
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