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 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: 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: 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 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: 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: Techniques disclosed herein include a reviewer interface for providing random-access review of point-of-sale (POS) video, visual indicators to point to point to objects within the point-of-sale video, and/or display of interactive statistical features. The system obtains video data of point-of-sale transactions, and corresponding transaction data correlated with the video data. The system identifies and displays transaction events of interest. In one embodiment, such a display includes displaying a visual indicator positioned at a spatial location within displayed video to identify a location of an object in the displayed video that corresponds to the transaction event identified as a transaction event of interest. The system also includes functionality to display visual indicators associated with transaction data to convey magnitude or quantity of transaction events of interest.

Abstract: According to one configuration, a computer environment includes a first monitoring system, a second monitoring system, and an analyzer. The first monitoring system produces video of an item being entered into a point of sale system. The second monitoring system monitors events associated with entering of the item into the point of sale system. The analyzer detects, via a communication from the second monitoring system, an occurrence of at least one event associated with entering of the item. Based on communication, the analyzer correlates the at least one event to a particular time in the video when the at least one event occurred.

Abstract: A system performs video identification of items involved with transactions, as captured in video data, and compares this item identification information with transaction data obtained from a transaction terminal such as a point-of-sale register to identify situations that are suspicious and may indicate fraudulent activity or operator error. In particular, the system as disclosed herein utilizes unique camera positions, and can use video data from more than one camera, to perform robust detections of fraudulent or suspicious activities associated with transactions in the transaction area. By obtaining video data originating from at least one video camera that monitors a transaction area, the system can automatically (i.e. no human involvement needed) analyze the video data to track items involved in a transaction in the transaction area.

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 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 by obtaining transaction data concerning at least one transaction that occurs at the transaction area. The system correlates the video data associated with the transaction area to the transaction data to identify specific video data captured during occurrence of that at least one transaction at the transaction area. Based a transaction classification indicated by the transaction data, the system processes the video data to identify appropriate visual indicators within the video data that correspond to the transaction classification.

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 by obtaining transaction data concerning at least one transaction that occurs at the transaction area. The system correlates the video data associated with the transaction area to the transaction data to identify specific video data captured during occurrence of that at least one transaction at the transaction area. Based a transaction classification indicated by the transaction data, the system processes the video data to identify appropriate visual indicators within the video data that correspond to the transaction classification.

Abstract: A security system including a security tag for attachment to an article sold in a retail establishment, the security tag being formed with a coil having a tuned resonant frequency. The coil is formed of a composition of materials that includes a flake-like conductive metal that is intermixed with a resin, solvent and additives to define a paste-like composition that is adhered to a substrate in a coil configuration. A neutralizer device is formed as part of the security system and is operated to produce a high intensity magnetic field that emits eddy currents for melting the coil and thereby disabling the security tag when the tag is placed in close proximity thereto.