Abstract: System and method for detecting a fraudulent activity at a barcode scanner is disclosed. The method issues an alert when the fraudulent activity is confirmed by comparing the visual signature of the item being transacted over the checkout terminal to the model visual signature. The model visual signature is obtained by averaging the collection of visual signature of the item gathered over a period of time. A human validation via a remote processor is employed to confirm the fraudulent activity verified by a computer.

Abstract: A system and method for replaying a security video at the time of a fraudulent incident at a point-of-sale checkout terminal is presented. The video playback takes place in real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the event. Examples of such events may include when a weight-based security alert is triggered at a self-checkout, when a cashier or customer has missed scanning an item at the checkout, or other possible events where rapid replay of relevant video is required.

Abstract: A system and method for verifying a non-scan item at a self-checkout point-of-sale terminals provided. A merchandise item that isn't scanned at the self-checkout terminal is identified by a detection system. Further, a user helper device is provided to verify the incident by prompting the shopper for an input. The user helper device is operably connected to the self-checkout point-of-sale terminal and the detection system to determine and verify a fraudulent incident occurring during a transaction activity by the shoppers. Further, a store attendant device is provided for human intervention when necessary.

Abstract: System and method for detecting a fraudulent activity at a barcode scanner is disclosed. The method issues an alert when the fraudulent activity is confirmed by comparing the visual signature of the item being transacted over the checkout terminal to the model visual signature. The model visual signature is obtained by averaging the collection of visual signature of the item gathered over a period of time. A human validation via a remote processor is employed to confirm the fraudulent activity verified by a computer.

Abstract: A system and method for verifying a non-scan item at a self-checkout point-of-sale terminal is provided. A merchandise item that isn't scanned at the self-checkout terminal is identified by a detection system. Further, a user helper device is provided to verify the incident by prompting the shopper for an input. The user helper device is operably connected to the self-checkout point-of-sale terminal and the detection system to determine and verify a fraudulent incident occurring during a transaction activity by the shoppers. Further, a store attendant device is provided for human intervention when necessary.

Abstract: A system and method for replaying a security video at the time of a fraudulent incident at a point-of-sale checkout terminal is presented. The video playback takes place in real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the event. Examples of such events may include when a weight-based security alert is triggered at a self-checkout, when a cashier or customer has missed scanning an item at the checkout, or other possible events where rapid replay of relevant video is required.

Abstract: A scan avoidance monitoring system detects instances in which a respective customer in a retail environment fails to properly scan or tally one or more retail items for purchase. To avoid generating false positives, such as flagging of events as scan avoidances when they really are not or likely are not scan avoidances, the scan avoidance system can be configured to require a level of confirmation by one or more other analyzers to flag a particular circumstance as a scan avoidance.

Abstract: Embodiments herein include novel ways of alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Although the discussion below uses a grocery store as an example retail environment, embodiments herein can be used in any type of retail environment.

Abstract: The present disclosure provides a system and method for creating at multilayer scene using a multiple visual input data. And injecting an image of an actor into the multilayer scene to produce a output video approximating a three-dimensional space which signifies depth by visualizing the actor in front of some layers and behind others. This is very useful for many situations where the actor needs to be on a display with other visual items but in a way that does not overlap or occlude those items. A user interacts with other virtual objects or items in a scene or even with other users visualized in the scene.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: System and method for detecting a fraudulent activity at a barcode scanner is disclosed. The method issues an alert when the fraudulent activity is confirmed by comparing the visual signature of the item being transacted over the checkout terminal to the model visual signature. The model visual signature is obtained by averaging the collection of visual signature of the item gathered over a period of time. A human validation via a remote processor is employed to confirm the fraudulent activity verified by a computer.

Abstract: Embodiments herein include novel ways of alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Although the discussion below uses a grocery store as an example retail environment, embodiments herein can be used in any type of retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: System and method for detecting a fraudulent activity at a barcode scanner is disclosed. The method issues an alert when the fraudulent activity is confirmed by comparing the visual signature of the item being transacted over the checkout terminal to the model visual signature. The model visual signature is obtained by averaging the collection of visual signature of the item gathered over a period of time. A human validation via a remote processor is employed to confirm the fraudulent activity verified by a computer.

Abstract: A computer system monitors a private network for presence of data traffic. In one embodiment, the private network is a retail environment in which a customer purchases an item scanned at a point-of-sale terminal. The data traffic can be point-of-sale transaction information generated in response to purchase of the item at the point-of-sale terminal. Subsequent to detecting presence of the data traffic in the network, the computer system analyzes the data traffic to identify private information in the data traffic. To protect the private information from improper disclosure to unauthorized entities, the computer system modifies the data traffic. Thus, techniques herein can include detecting occurrence of one or more point-of-sale events in a private network, anonymizing the event information (e.g., data traffic), and forwarding the anonymized event information associated with the point-of-sale events out of the private network to a remote resource.

Abstract: Embodiments herein include novel ways of alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Although the discussion below uses a grocery store as an example retail environment, embodiments herein can be used in any type of retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: Embodiments herein include novel ways of alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Although the discussion below uses a grocery store as an example retail environment, embodiments herein can be used in any type of retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.