Abstract: Systems and methods for automating an image rejection process. Features including texture, spatial structure, and image quality characteristics can be extracted from one or more images to train a classifier. Features can be calculated with respect to a test image for submission of the features to the classifier, given an operating point corresponding to a desired false positive rate. One or more inputs can be generated from the classifier as a confidence value corresponding to a likelihood of, for example: a license plate being absent in the image, the license plate being unreadable, or the license plate being obstructed. The confidence value can be compared against a threshold to determine if the image(s) should be removed from a human review pipeline, thereby reducing images requiring human review.

Abstract: A method for removing false foreground image content in a foreground detection process performed on a video sequence includes, for each current frame, comparing a feature value of each current pixel against a feature value of a corresponding pixel in a background model. The each current pixel is classified as belonging to one of a candidate foreground image and a background based on the comparing. A first classification image representing the candidate foreground image is generated using the current pixels classified as belonging to the candidate foreground image. The each pixel in the first classification image is classified as belonging to one of a foreground image and a false foreground image using a previously trained classifier. A modified classification image is generated for representing the foreground image using the pixels classified as belonging to the foreground image while the pixels classified as belonging to the false foreground image are removed.

Abstract: A computer vision system (100) operates to monitor an environment (e.g., such as a restaurant, store or other retail establishment) including a resource located therein (e.g., such as a restroom, a dining table, a drink, condiment or supply dispenser, a trash receptacle or a tray collection rack). The system includes: an image source or camera (104) that supplies image data (130) representative of at least a portion of the environment monitored by the system, the portion including the resource therein; and an event detection device (102) including a data processor (112) and operative to detect an event involving the resource. Suitably, the event detection device is arranged to: (i) be selectively configurable by a user to define the event involving the resource; (ii) receive the image data supplied by the image source; (iii) analyze the received image data to detect the defined event; and (iv) output a notification in response to detecting the defined event.

Abstract: A system and method for detection of a goods-received event includes acquiring images of a retail location including a vehicular drive-thru, determining a region of interest within the images, the region of interest including at least a portion of a region in which goods are delivered to a customer, and analyzing the images using at least one computer vision technique to determine when goods are received by a customer. The analyzing includes identifying at least one item belonging to a class of items, the at least one item's presence in the region of interest being indicative of a goods-received event.

Abstract: A system for delivering one of a good and service to a customer in a retail environment includes a computer located at an order station. The computer is configured to receive an order for the one good and service. The system includes a first image capture device in communication with the computer. The first image capture device captures a first image of a customer ordering the one good and service in response to the order being submitted. The system further includes a wearable computer peripheral device configured to acquire the first image from the first image capture device and electronically display the first image to a user tasked with delivering the one good and service while carrying the second wearable computer peripheral device. In this manner, an identity of the customer can be compared against the first image upon a delivery of the one good and service.

Abstract: A method for removing false foreground image content in a foreground detection process performed on a video sequence includes, for each current frame, comparing a feature value of each current pixel against a feature value of a corresponding pixel in a background model. The each current pixel is classified as belonging to one of a candidate foreground image and a background based on the comparing. A first classification image representing the candidate foreground image is generated using the current pixels classified as belonging to the candidate foreground image. The each pixel in the first classification image is classified as belonging to one of a foreground image and a false foreground image using a previously trained classifier. A modified classification image is generated for representing the foreground image using the pixels classified as belonging to the foreground image while the pixels classified as belonging to the false foreground image are removed.