Abstract: A system for information discovery of items, such as individuals or objects, from video-based tracks. The system may compute similarities of characteristics of the items and present the results in a matrix form. A similarity portrayal may have nodes representing the items with edges between the nodes. The edges may have weights in the form of vectors indicating similarities of the characteristics between the nodes situated at the ends of the edges. The edges may be augmented with temporal and spatial properties from the tracks which cover the items. These properties may play a part in a multi-objective presentation of information about the items in terms of a negative or supportive basis. The presentation may be partitioned into clusters which may lead to a merger of items or tracks. The system may pave a way for higher-level information discovery such as video-based social networks.

Abstract: A system for social network construction. Video analytics and association may be used to develop a social network. Also, social groups may be developed from temporal proximity of persons. In the case of several social networks, they may be collapsed into one network or a weighted graph that mining algorithms can handle.

Abstract: A system for information discovery of items, such as individuals or objects, from video-based tracks. The system may compute similarities of characteristics of the items and present the results in a matrix form. A similarity portrayal may have nodes representing the items with edges between the nodes. The edges may have weights in the form of vectors indicating similarities of the characteristics between the nodes situated at the ends of the edges. The edges may be augmented with temporal and spatial properties from the tracks which cover the items. These properties may play a part in a multi-objective presentation of information about the items in terms of a negative or supportive basis. The presentation may be partitioned into clusters which may lead to a merger of items or tracks. The system may pave a way for higher-level information discovery such as video-based social networks.

Abstract: Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103).

Abstract: A method includes receiving at least one search parameter, where the at least one search parameter defines one or more restrictions on types of event patterns. The method also includes searching a collection of historical events associated with a process control system. The method further includes identifying one or more groups of alarms each having a pattern that satisfies the one or more restrictions. In addition, the method includes outputting information identifying the one or more groups of alarms. The method could also include receiving a selection of at least one of the one or more identified groups of alarms. The method could further include notifying one or more components in the process control system to begin dynamically suppressing alarms in the at least one selected group of alarms.

Abstract: A system for social network construction. Video analytics and association may be used to develop a social network. Also, social groups may be developed from temporal proximity of persons. In the case of several social networks, they may be collapsed into one network or a weighted graph that mining algorithms can handle.

Abstract: Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103).

Abstract: Methods of using motion-texture analysis to perform video analytics are disclosed. One method includes selecting a plurality of frames from a video sequence, analyzing motion textures in the plurality of frames to identify a flow, extracting features from the flow, and characterizing the extracted features to perform activity recognition. Another method includes selecting a plurality of frames from a video sequence, analyzing motion textures in the plurality of frames to identify a flow, extracting first features from the flow, comparing the first features with second features extracted during a previous training phase, and based on the comparison, determining whether the first features indicate abnormal activity. Another method includes partitioning a given frame in a video sequence into a plurality of patches, forming a vector model for each patch by analyzing motion textures associated with that patch, and clustering patches having vector models that show a consistent pattern.