Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking objects over a sequence of images. In one aspect, a process includes receiving data specifying visual attributes of a particular object detected in an image. Data specifying visual attributes of a tracked object is identified. A level of similarity is determined between the particular object and the tracked object based on comparison of visual attributes of the particular object with corresponding visual attributes of the tracked object. A first normalization function is used to normalize a level of similarity between first corresponding visual attributes of the particular object and the tracked object. A second normalization function is used to normalize a level of similarity between second corresponding visual attributes of the particular object and the tracked object. A determination is made, based on the level of similarity, whether the particular object matches the tracked object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking objects over a sequence of images. In one aspect, a process includes receiving data specifying visual attributes of a particular object detected in an image. Data specifying visual attributes of a tracked object is identified. A level of similarity is determined between the particular object and the tracked object based on comparison of visual attributes of the particular object with corresponding visual attributes of the tracked object. A first normalization function is used to normalize a level of similarity between first corresponding visual attributes of the particular object and the tracked object. A second normalization function is used to normalize a level of similarity between second corresponding visual attributes of the particular object and the tracked object. A determination is made, based on the level of similarity, whether the particular object matches the tracked object.

Abstract: This document describes systems, methods, devices, and other techniques for accessing a first video showing a first two-dimensional scene of an environment and captured by a first camera located in the environment having a first field of view; detecting one or more objects shown in the first video; analyzing the first video to determine one or more features of each of the detected objects shown in the first video; accessing a second video showing a second 2D scene of the environment and captured by a second camera located in the environment having a second field of view; detecting one or more objects shown in the second video; analyzing the second video to determine one or more features of each of the detected objects shown in the second video; and correlating one or more objects shown in the first video with one or more objects shown in the second video.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: This document describes systems, methods, devices, and other techniques for video camera self-calibration based on video information received from the video camera. In some implementations, a computing device receives video information characterizing a video showing a scene from a field of view of a video camera; detects an object that appears in the scene of the video; identifies a visual marking that appears on the detected object; determines a particular visual marking among a plurality of pre-defined visual markings that matches the visual marking that appears on the detected object; identifies one or more object characteristics associated with the particular visual marking; evaluates one or more features of the video with respect to the one or more object characteristics; and based on a result of evaluating the one or more features of the video with respect to the one or more object characteristics, sets a parameter of the video camera.

Abstract: This document describes systems, methods, devices, and other techniques for video camera scene translation. In some implementations, a computing device accesses a first video that shows a first 2D scene of an environment; defines a trip wire at a first position of the first 2D scene; obtains a 3D model of at least a portion of the environment shown in the first 2D scene; maps the trip wire from the first position of the first 2D scene to a first position of the 3D model that corresponds to the first position of the first 2D scene; accesses a second video that shows a second 2D scene of the environment; and projects the trip wire from the first position of the 3D model to a first position of the second 2D scene that corresponds to a same location in the environment as the first position of the first 2D scene.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: This document describes systems, methods, devices, and other techniques for accessing a first video showing a first two-dimensional scene of an environment and captured by a first camera located in the environment having a first field of view; detecting one or more objects shown in the first video; analyzing the first video to determine one or more features of each of the detected objects shown in the first video; accessing a second video showing a second 2D scene of the environment and captured by a second camera located in the environment having a second field of view; detecting one or more objects shown in the second video; analyzing the second video to determine one or more features of each of the detected objects shown in the second video; and correlating one or more objects shown in the first video with one or more objects shown in the second video.

Abstract: This document describes systems, methods, devices, and other techniques for video camera self-calibration based on video information received from the video camera. In some implementations, a computing device receives video information characterizing a video showing a scene from a field of view of a video camera; detects an object that appears in the scene of the video; identifies a visual marking that appears on the detected object; determines a particular visual marking among a plurality of pre-defined visual markings that matches the visual marking that appears on the detected object; identifies one or more object characteristics associated with the particular visual marking; evaluates one or more features of the video with respect to the one or more object characteristics; and based on a result of evaluating the one or more features of the video with respect to the one or more object characteristics, sets a parameter of the video camera.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect events shown within digital video content captured by one or more video cameras during a prior time period, and predict an occurrence of an additional event during a future time period based on time-varying patterns among the detected events. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may establish a predictive model that identifies one or more time-varying patterns in event parameter values that characterize the detected events within the prior time period. Based on an outcome of the predictive model, the computing system may determine an expected value of one of the event parameters during the second time period.

Abstract: According to an example, a face capture and matching system may include a memory storing machine readable instructions to receive captured images of an area monitored by an image capture device, and detect one or more faces in the captured images. The memory may further store machine readable instructions to track movement of the one or more detected faces in the area monitored by the image capture device, and based on the one or more tracked detected faces, select one or more images from the captured images to be used for identifying the one or more tracked detected faces. The memory may further store machine readable instructions to select one or more fusion techniques to identify the one or more tracked detected faces using the one or more selected images. The face capture and matching system may further include a processor to implement the machine readable instructions.