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: 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 verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

Abstract: A processing resource receives original image data by a surveillance system. The original image data captures at least private information and occurrence of activity in a monitored region. The processing resource applies one or more transforms to the original image data to produce transformed image data. Application of the one or more transforms sufficiently distorts portions of the original image data to remove the private information. The transformed image data includes the distorted portions to prevent access to the private information. However, the distorted portions of the video include sufficient image detail to discern occurrence of the activity in the retail environment.

Abstract: A verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

Abstract: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

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: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

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: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

Abstract: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

Abstract: A processing resource receives original image data by a surveillance system. The original image data captures at least private information and occurrence of activity in a monitored region. The processing resource applies one or more transforms to the original image data to produce transformed image data. Application of the one or more transforms sufficiently distorts portions of the original image data to remove the private information. The transformed image data includes the distorted portions to prevent access to the private information. However, the distorted portions of the video include sufficient image detail to discern occurrence of the activity in the 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: A processing resource receives original image data by a surveillance system. The original image data captures at least private information and occurrence of activity in a monitored region. The processing resource applies one or more transforms to the original image data to produce transformed image data. Application of the one or more transforms sufficiently distorts portions of the original image data to remove the private information. The transformed image data includes the distorted portions to prevent access to the private information. However, the distorted portions of the video include sufficient image detail to discern occurrence of the activity in the 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: A verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

Abstract: The present disclosure includes a system in which a video displayer, having access to video surveillance data and point-of-sale transaction data, provides random-access review of point-of-sale transactional video. Embodiments herein provide a new method of reviewing security video of a transaction taking place at a retail establishment's point-of-sale transaction area, or register. Embodiments herein enable random and instantaneous access to any number of transaction events that may be of interest to a loss prevention investigator. In one embodiment, a video displayer obtains video data comprising a point-of-sale transaction occurring at a point-of-sale transaction area, and transaction data from a point-of-sale terminal. The video displayer displays video of individual transaction events from a larger transaction, and visually modifies displayed video at a moment in the video when a transaction event occurs.

Abstract: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.