Abstract: A content analysis engine receives video input and performs analysis of the video input to produce one or more gross change primitives. A view engine coupled to the content analysis engine receives the one or more gross change primitives from the content analysis engine and provides view identification information. A rules engine coupled to the view engine receives the view identification information from the view engine and provides one or more rules based on the view identification information. An inference engine performs video analysis based on the one or more rules provided by the rules engine and the one or more gross change primitives.

Abstract: Provided is a computer-implemented method for processing one or more video frames. The meth can include generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames; classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing, by the processor, a listing of detected targets based on the one or more classes.

Abstract: A video surveillance system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Abstract: A method and system using face tracking and object tracking is disclosed. The method and system use face tracking, location, and/or recognition to enhance object tracking, and use object tracking and/or location to enhance face tracking.

Abstract: A method and system for performing video content analysis based on two-dimensional image data and depth data are disclosed. Video content analysis may be performed on the two-dimensional image data, and then the depth data may be used along with the results of the video content analysis of the two-dimensional data for tracking and event detection.

Abstract: Provided is a computer-implemented method for processing one or more video frames. The meth can include generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames; classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing, by the processor, a listing of detected targets based on the one or more classes.

Abstract: Various implementations of the invention perform facial recognition on a target image compared against a color image from an image gallery, where the target image was acquired by an infrared-sensitive camera of a target that was illuminated with infrared light. According to various implementations of the invention, a blue component and a green component of the pixels in the color image are suppressed or eliminated, and facial recognition is performed between the target image and the color-suppressed image.

Abstract: A method and system for monitoring a retail environment by performing video content analysis based on two-dimensional image data and depth data are disclosed. Accuracy in customer actions to provide assistance, change marketing behavior, safety and theft, for example, is increase by analyzing video containing two-dimensional image data and associated depth data. Height data may be obtained from depth data to assist in object detection, object classification (e.g., detection a customer or inventory) and/or event detection.

Abstract: Systems and methods for controlling camera settings of a camera to improve detection of faces in an uncontrolled environment are described. A first image is received from the camera, where the first image is captured by the camera at a first set of camera settings. A face is detected in the first image. The camera is adjusted to a second set of camera settings based on the detected face, where the second set of camera settings different from the first set of camera settings. A second image is received from the camera, where the second image is captured by the camera at the second set of camera settings. The face is detected in the second image. A quality metric of the face in the second image is determined where the quality metric is indicative of an image quality of the face in the second image.

Abstract: A sensing device includes: a video imager to obtain a video; a processing unit to receive and process the video from the video imager; and a communication channel to output non-imagery signals.

Abstract: A method and system for monitoring buildings (including houses and office buildings) by performing video content analysis based on two-dimensional image data and depth data are disclosed. Occupation and use of such buildings may be monitored with higher accuracy to provide higher energy efficiency usage, to control operation of components therein, and/or provide better security. Height data may be obtained from depth data to provide greater reliability in object detection, object classification and/or event detection.

Abstract: Systems and methods for controlling camera settings of a camera to improve detection of faces in an uncontrolled environment are described. A first image is received from the camera, where the first image is captured by the camera at a first set of camera settings. A face is detected in the first image. The camera is adjusted to a second set of camera settings based on the detected face, where the second set of camera settings different from the first set of camera settings. A second image is received from the camera, where the second image is captured by the camera at the second set of camera settings. The face is detected in the second image. A quality metric of the face in the second image is determined where the quality metric is indicative of an image quality of the face in the second image.

Abstract: Various implementations of the invention perform facial recognition on a target image compared against a color image from an image gallery, where the target image was acquired by an infrared-sensitive camera of a target that was illuminated with infrared light. According to various implementations of the invention, a blue component and a green component of the pixels in the color image are suppressed or eliminated, and facial recognition is performed between the target image and the color-suppressed image.

Abstract: Various implementations of the invention perform facial recognition on a target image compared against a color image from an image gallery, where the target image was acquired by an infrared-sensitive camera of a target that was illuminated with infrared light. According to various implementations of the invention, a blue component and a green component of the pixels in the color image are suppressed or eliminated, and facial recognition is performed between the target image and the color-suppressed image.

Abstract: A system and method for improving a video characteristic of a video stream is described. According to various implementations of the invention, a changed region between a later-in-time image frame and an earlier-in-time image frame and an unchanged region between such two image frames are determined. A new improvement to the video characteristic is determined and applied to the changed region of the later-in-time image frame. A prior improvement to the video characteristic that was determined for the earlier-in-time image frame is applied to the unchanged region of the later-in-time image frame.

Abstract: A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Abstract: A monitoring method and system are disclosed. In one embodiment, a method includes monitoring the health of a person in a home. The method includes capturing a video sequence from a first camera disposed within the home, including capturing two-dimensional image data for the video sequence; receiving depth data corresponding to the two-dimensional data, and associating the depth data with the video sequence as metadata; setting a plurality of events to monitor associated with the person, the events defined to include actions captured from the first camera, at least a first event including the person's body being in a particular bodily position and performing video content analysis on the video sequence to determine whether the events have occurred.

Abstract: Methods and systems are provided for monitoring a point of sale (POS) transaction. Operations performed by the methods and systems include generating POS primitives by processing non-video data of a transaction recorded at the POS terminal. The operations also include generating video primitives by processing video data of the transaction recorded at the POS terminal. The operations further include determining that the transaction comprises and exceptional transaction by comparing the non-video data and/or the video data to exceptional transaction rules. Additionally, the operations include determining that that exceptional transaction comprises a verified exceptional transaction by generating a video event based on the vide primitives and a corresponding video rule.

Abstract: A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Abstract: A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.