Abstract: A case with an integrated or attached credential reader; when a user presents a valid credential (such as a biometric identity like a fingerprint, handprint, or face), the case is unlocked and the user can take items from the case. Sensors in the case such as cameras and weight sensors detect items taken by the user; these are automatically charged to the user or added to the user's shopping cart. The credential reader may be on a door handle so that as the user reaches for the handle the credential is automatically captured. The entire shopping experience may be quick and seamless since the user's credential may be captured automatically and the items the user takes may be accounted for automatically. Embodiments may have a door that opens automatically when the credential is accepted and closes and locks automatically when sensors detect that the user has retracted from the case.

Abstract: An automated store that calculates a confidence score for virtual shopping carts of shoppers, and selects carts for manual review based on these scores. Carts with low confidence scores may be more likely to contain errors, so prioritizing manual review of these carts is a cost-effective method of improving overall accuracy. A cart confidence score may be a function of factors such as confidence in the trajectory of the shopper generated by the store tracking system, confidence in the events (such as taking an item from a shelf) that affect the cart, and confidence that events are attributed to the correct shopper. Situations that make tracking, item identification, or attribution more complex may reduce confidence levels. For example, attribution confidence may be low when multiple shoppers are near an event, and item confidence may be low if the probabilistic classifier that identifies the item assigns nontrivial probabilities to multiple items.

Abstract: A video surveillance system with real-time object re-identification capabilities, which employs an object re-identification algorithm and an edge computing architecture. An operator monitors video images from the multiple cameras, and when a target object is observed, a target image containing the object is transmitted to all video cameras for object re-identification. Each video camera has dedicated processing circuitry that performs an object re-identification algorithm to identify the target in video images captured by that camera in real time. The algorithm calculates a frequency domain similarity measure between the target image and test images captured by the camera. The similarity measure in the frequency domain is calculated as a dot product of the 1D discrete Fourier transforms of the target image data and of the test image data. The multiple cameras also transmit object re-identification results to each other to achieve more efficient and intelligent object re-identification.

Abstract: A video surveillance system with real-time object re-identification capabilities, which employs an object re-identification algorithm and an edge computing architecture. An operator monitors video images from the multiple cameras, and when a target object is observed, a target image containing the object is transmitted to all video cameras for object re-identification. Each video camera has dedicated processing circuitry that performs an object re-identification algorithm to identify the target in video images captured by that camera in real time. The algorithm calculates a frequency domain similarity measure between the target image and test images captured by the camera. The similarity measure in the frequency domain is calculated as a dot product of the 1D discrete Fourier transforms of the target image data and of the test image data. The multiple cameras also transmit object re-identification results to each other to achieve more efficient and intelligent object re-identification.