International Business Machines Corporation

Prasad Gabbur

Sharathchandra UmapathiRao Pankanti

Quanfu Fan

Sachiko Miyazawa

Hoang Trinh

Arun Hampapur

Lei Ding

Jiyan Pan

Events are classified through string pattern recognition. Text labels are assigned to image primitives in a time-ordered set of training images and to related time-ordered transactions in an associated training transaction log in a combined time-ordered training string of text labels as a function of image types. Transactions are labeled in a training transaction log with a transaction label, a training primitive image of a start of a transaction with a start image text label, a training primitive of an entry of a transaction into the log with an entry image text label, and a training primitive of a conclusion of a transaction with an ending image text label. Positive subset string patterns are discovered representing true events from the combined time-ordered training string of text labels, and negative subset string patterns defined by removing single transaction primitive labels from the positive subset string patterns.

Event detection through pattern discovery

Visual content in images captured from a scene by a camera in each of a plurality of different pose settings are analyzed to determine predicted occurrences of a transaction associated with the visual content in each pose, which are compared with actual transaction occurrence data to generate performance values for each pose as a function difference between the predicted and actual transactions. Optimized poses are chosen having the best performance value, wherein a camera controller may place the camera in the optimum pose for use in monitoring the scene and generating the primitives of interest associated with the transactions.

Visual content-aware automatic camera adjustment

Determination of human behavior from an alignment of data streams includes acquiring visual image primitives from a video input comprising visual information relevant to a human activity. The primitives are temporally aligned to an optimally hypothesized sequence of primitives transformed from a sequence of transactions as a function of a distance metric between the observed primitive sequence and the transformed primitive sequence. More particularly, transforming includes comparing the distance metric costs and choosing and performing the lowest cost of temporally matching the observed primitives to one or more transactions, deleting a primitive, or associating a primitive with a pseudo transaction marker. Accordingly, alerts are issued based on analysis of the transformation of primitives.

Event determination by alignment of visual and transaction data

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Prasad Gabbur