Abstract: A computer system for managing an in-store aisle, the computer system including a camera that captures a video in a retail store, and a computer connected to the camera, wherein the computer extracts a position of at least one customer who appears in the video captured by the camera and one or more kinds of emotion of the at least one customer at the position based on the video captured by the camera, and stores and manages a most-extracted emotion of the at least one customer in an aisle in which the at least one customer is positioned as information on the aisle.

Abstract: A computer system for managing a workforce includes a camera that captures a video in a retail store, and a computer connected to the camera, wherein when the computer determines that there is a customer not being assisted by an employee and lingering in a particular place in the store based on the video captured by the camera in the store, the computer directs the employee or another employee to assist the customer lingering in the particular place while prioritizing the customer lingering in the particular place over another customer lingering in a place other than the particular place.

Abstract: A method is provided for detecting a threat in a distributed multi-camera surveillance system. The method includes: monitoring movement of an object in a field of view of a first camera using software installed at the first camera; detecting a suspicious object at the first camera when movement of the object does not conform with a motion flow model residing at the first camera; sending a tracking request from the first camera to a second camera upon detecting the suspicious object at the first camera; monitoring movement of the object in a field of view of the second camera using software installed at the second camera; assigning threat scores at the second camera when the movement of the object does not conform with a motion flow model residing at the second camera; and generating an alarm based in part on the threat scores detected at the first camera and the second camera.

Abstract: A surveillance system which improves accuracy and robustness of abnormal behavior detection of a monitored object traversing a space includes a metadata processing module, a model building module, and a behavior assessment module. The metadata processing module generates trajectory information for a monitored object and determines attributes of the monitored object. The model building module generates and updates normal motion models based on at least one of the trajectory information, the attributes, and an abnormal behavior score. The behavior assessment module generates the abnormal behavior score based on one of a plurality of methods. A first one of the plurality of methods defines wrong direction behavior. A second one of the plurality of methods defines wandering/loitering behavior. A third one of the plurality of methods defines speeding behavior.

Abstract: A video surveillance system is disclosed. The system includes a model database storing a plurality of models and a vector database storing a plurality of vectors of recently observed trajectories. The system includes a model building module that builds a new motion model corresponding to the motion data of the current trajectory data structure. The system generates a current trajectory data structure having motion data and abnormality scores. The system also includes a database purging module configured to determine a subset of vectors that is most similar to the current trajectory data structure based on a measure of similarity between the subset of vectors and the current trajectory data structure. The database purging module is further configured to replace one of the motion models in the model database with the new motion model based on an amount of vectors in the subset vectors the recentness of the subset of vectors.

Abstract: A system for improving site operations by detecting abnormalities includes a first sensor abnormality detector connected to a first sensor and configured to learn a first normal behavior sequence, a second sensor abnormality detector connected to a second sensor and configured to learn a second normal behavior sequence, an abnormality correlation server configured to receive abnormally scored first sensor data and abnormally scored second sensor data, the abnormality correlation server further configured to correlate the received abnormally scored first sensor data and abnormally scored second sensor data sensed at the same time by the first and second sensors and determine an abnormal event; and an abnormality report generator configured to generate an abnormality report based on the correlated the received abnormally scored first sensor data and abnormally scored second sensor data.

Abstract: Method of notifying a user of site abnormalities via an application configured to access an event server having a first sensor abnormality detector connected to a first sensor, for detecting first abnormal behavior of first sub-events sensed by the first sensor, the first abnormal behavior corresponding to a first abnormal behavior value, a second sensor abnormality detector connected to a second sensor, for detecting second abnormal behavior of second sub-events sensed by the second sensor of a type different from the first sensor, the second abnormal behavior corresponding to a second abnormal behavior value, a correlator for correlating the first and second abnormal behavior values and logging correlated values as a composite event, a data store, the application having a viewer configured to show, on the device, data associated with a plurality of composite events, the viewer further configured to display the plurality of composite events in a temporal order.

Abstract: A system and method for predictive abnormal behavior detection is disclosed. The system receives surveillance data such as video data and can create and update a plurality of prediction models. The system may also receive video data relating to a moving object and may generate a prediction of the future locations of the moving object based on the generated prediction models. The predicted motion may be scored by a scoring engine to determine if the predicted motion is unsafe or otherwise undesirable.

Abstract: A multi-perspective context sensitive behavior assessment system includes an adaptive behavior model builder establishing a real-time reference model that captures intention of motion behavior. It operates by modeling outputs of multiple user defined scoring functions with respect to multiple references of application specific target areas of interest. The target areas have criticality values representing a user's preference regarding the target areas with respect to one another. The outputs of the scoring functions are multiplied by the critically values to form high level sequences of representation that are communicated to the user.

Abstract: A video surveillance system is disclosed. The system includes a model database storing a plurality of models and a vector database storing a plurality of vectors of recently observed trajectories. The system includes a model building module that builds a new motion model corresponding to the motion data of the current trajectory data structure. The system generates a current trajectory data structure having motion data and abnormality scores. The system also includes a database purging module configured to determine a subset of vectors that is most similar to the current trajectory data structure based on a measure of similarity between the subset of vectors and the current trajectory data structure. The database purging module is further configured to replace one of the motion models in the model database with the new motion model based on an amount of vectors in the subset vectors the recentness of the subset of vectors.

Abstract: A surveillance system implements an architecture and process to support real-time abnormal behavior assessment operations in a distributed scalable sensor network. An automated behavior model builder generates behavior models from sensor data. A plurality of abnormal behavior scoring engines operating concurrently to generate abnormal behavior assessment models by scoring the behavior models. An execution performance manager performs fast switching of behavior models for the abnormal behavior scoring engines. The execution performance manager performs detection of abnormal behavior score distribution characteristic deviation by comparing a current abnormal behavior assessment model to a pre-recorded abnormal behavior assessment model. The execution performance manager selects a pre-recorded behavior model for the abnormal behavior scoring engines when the deviation exceeds a predetermined threshold.

Abstract: The intelligent, automated surveillance system collects the interactions between the security personal and the surveillance system during the handling of an alarm. Each alarm is modeled as a “transaction” and each operation/action that a security personal executes modeled as an “event” within the transaction. The collected events within the transaction are in partial order. Furthermore, the system provides a scoring system for a security manager to evaluate the performance of the security guard. The score of the sequence of actions that the security guard performed manually and the system performed automatically for each set of dependent alarms are used to decide future sequence of operation. Security guards can overwrite the automatic sequencing of actions with manual sequence of operations.

Abstract: A system and method for predictive abnormal behavior detection is disclosed. The system receives surveillance data such as video data and can create and update a plurality of prediction models. The system may also receive video data relating to a moving object and may generate a prediction of the future locations of the moving object based on the generated prediction models. The predicted motion may be scored by a scoring engine to determine if the predicted motion is unsafe or otherwise undesirable.

Abstract: A surveillance system improves accuracy and robustness of abnormal behavior detection of a monitored object traversing a space includes a metadata processing module, a model building module, and a behavior assessment module. The metadata processing module generates trajectory information for a monitor object and determines attributes of the monitored object. The model building module at least one of generates and updates normal motion models based on at least one of the trajectory information, the attributes, and an abnormal behavior score. The behavior assessment module generates the abnormal behavior score based on one of a plurality of methods. A first one of the plurality of methods defines wrong direction behavior. A second one of the plurality of methods defines wandering/loitering behavior. A third one of the plurality of methods defines speeding behavior.

Abstract: A system and method for managing video storage on a video surveillance system is disclosed. The system calculates an importance score for a video segment based on a weighted average of multiple scores corresponding to the video event. The multiple scores include an event correlation score correlating a video event with a plurality of other video events, an abnormality score indicating the abnormality of the observed event, a user entered score, a score relating to the number of times a specific location was visited by a moving object, the amount of times a video has been retrieved, and a predicted future storage space. The importance score may be used to determine a video retention operation, such as retaining the video event, purging the video event, reducing the video quality of the event, or storing the video in mix-reality format.

Abstract: An Intelligent, dynamic, long-term digital Surveillance Media storage System (ISMS), proposes an intelligent, dynamic, digital media storage mechanism for digital surveillance applications that can analyze media streams in real-time and provide flexible, scalable, and self-adjustable encoding and indexing mechanisms based on actual needs for digital surveillance systems. The system includes several subsystems, such as: (1) a media stream retriever/receiver; (2) a content-based description generator; (3) an intelligent engine with metadata description storage and media file storage (4) an event manager; (5) a media stream writer; and (6) an encoding optimizer.

Abstract: A surveillance system generally includes a data capture module that collects sensor data. A scoring engine module receives the sensor data and computes at least one of an abnormality score and a normalcy score based on the sensor data, at least one dynamically loaded learned data model, and a learned scoring method. A decision making module receives the at least one of the abnormality score and the normalcy score and generates an alert message based on the at least one of the abnormality score and the normalcy score and a learned decision making method to produce progressive behavior and threat detection.

Abstract: A multi-perspective context sensitive behavior assessment system includes an adaptive behavior model builder establishing a real-time reference model that captures intention of motion behavior. It operates by modeling outputs of multiple user defined scoring functions with respect to multiple references of application specific target areas of interest. The target areas have criticality values representing a user's preference regarding the target areas with respect to one another. The outputs of the scoring functions are multiplied by the critically values to form high level sequences of representation that are communicated to the user.

Abstract: A resource manager transforms the MPEG7 descriptors into an active and composite storage, processing, and presentation component in a storage area network (e.g. iSCSI). The active component can enable fast search and retrival operation directly on the logical disk blocks which maps well with the multimedia video stream and images data. It can also support content addressable search on the video and image files and play the media blocks directly. In addition, it enables a dynamic configuration of a media processing function after it is loaded in an execution environment. The data block is moved from the iSCSI interface directly into processing modules and presentation modules via decoder, buffer, and constraint control units.

Abstract: An associative vector storage system has an encoding engine that takes input vectors, and generates transformed coefficients for a tunable number of iterations. Each iteration performs a complete transformation to obtain coefficients, thus performing a process of iterative transformations. The encoding engine selects a subset of coefficients from the coefficients generated by the process of iterative transformations to form an approximation vector with reduced dimension. A data store stores the approximation vectors with a corresponding set of meta data containing information about how the approximation vectors are generated. The meta data includes one or more of the number of iterations, a projection map, quantization, and statistical information associated with each approximation vector. A search engine uses a comparator module to perform similarity search between the approximation vectors and a query vector in a transformed domain.