Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: Methods, systems, and techniques for classifying and/or detecting objects using visible and invisible light images. A visible light image and an invisible light image are received at a convolutional neural network (CNN). The visible light image depicts a region-of-interest imaged using visible light. The invisible light image depicts at least a portion of the region-of-interest imaged using invisible light, and at least one of the images depicts an object-of-interest within the portion of the region-of-interest shared between the images. The CNN then classifies and/or detects the object-of-interest using the images. The CNN may be trained to perform this classification and/or detection using pairs of visible and invisible light training images.

Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of detected objects, each of which has a signature representing features of the detected object. The match classifier matches object instances by analyzing data derived from the signatures. The match classifier determines whether the signatures match.

Abstract: There are described methods and systems for facilitating identification of an object-of-interest. A face similarity score and a body similarity score of a query image relative to a gallery image are determined. A fused similarity score of the query image relative to the gallery image is determined by applying a relationship between the face similarity score, the body similarity score, and the fused similarity score. The fused similarity score is indicative of whether or not the object-of-interest and the potential object-of-interest are the same object-of-interest. For example, a machine learning process is used to fuse the face similarity score and the body similarity into the fused similarity score. The process is repeated for multiple gallery images. The gallery images may then be ranked according to their respective fused similarity scores.

Abstract: A video surveillance system having a plurality of aerial camera devices such as cameras on drones operable from a plurality of docking stations. Each station has a dock for receiving, charging, and controlling the aerial camera devices.

Abstract: A method, system and computer program product for interactively identifying same individuals or objects present in video recordings is disclosed. When a thumbnail in a set of thumbnails is selected, new information is obtained. The new information may be that an individual or object is present in the portion of the video recording associated with the thumbnail. A search can be carried out for the individual or object based on the new information. The search generates new match likelihoods for each of displayed thumbnails within a user interface page. The displayed thumbnails are re-ordered based on the new match likelihoods.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A video surveillance system having a plurality of aerial camera devices such as cameras on drones operable from a plurality of docking stations. Each station having a dock for receiving, charging, and controlling the aerial camera devices.

Abstract: A camera device includes a camera module that captures a scene corresponding to a field of view of the camera module. A wireless power transmitter of the camera devices transmits wireless power to a battery-powered external sensing device. A transceiver is further operable for receiving from the external sensing device sensed data. A network module transmits the image and sensed data to an external networked device. A power supply receives power over a wired connection and supplies power to the camera module, transceiver, networking module and wireless transmitter. The external sensing device may be an access control device that includes a sensor for sensing the presence of a machine readable toke and reading an identifier of the physical token. The access control device further includes a lock controller for selectively locking and unlocking a physical lock.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A method, system and computer program product for interactively identifying same individuals or objects present in video recordings is disclosed. When a thumbnail in a set of thumbnails is selected, new information is obtained. The new information may be that an individual or object is present in the portion of the video recording associated with the thumbnail. A search can be carried out for the individual or object based on the new information. The search generates new match likelihoods for each of displayed thumbnails within a user interface page. The displayed thumbnails are re-ordered based on the new match likelihoods.

Abstract: A camera device includes a camera module that captures a scene corresponding to a field of view of the camera module. A wireless power transmitter of the camera devices transmits wireless power to a battery-powered external sensing device. A transceiver is further operable for receiving from the external sensing device sensed data. A network module transmits the image and sensed data to an external networked device. A power supply receives power over a wired connection and supplies power to the camera module, transceiver, networking module and wireless transmitter. The external sensing device may be an access control device that includes a sensor for sensing the presence of a machine readable toke and reading an identifier of the physical token. The access control device further includes a lock controller for selectively locking and unlocking a physical lock.

Abstract: There is provided an appearance search system comprising one or more cameras configured to capture video of a scene, the video having images of objects. The system comprises one or more processors and memory comprising computer program code stored on the memory and configured when executed by the one or more processors to cause the one or more processors to perform a method. The method comprises identifying one or more of the objects within the images of the objects. The method further comprises implementing a learning machine configured to generate signatures of the identified objects and generate a signature of an object of interest. The system further comprises a network configured to send the images of the objects from the camera to the one or more processors.

Abstract: Methods, systems, and techniques for sharing layers between convolutional neural networks (CNNs). A data processing system may include a first and a second CNN that share a first group of layers. The first CNN may include the first group of layers in series with a second group of layers and be configured such that data for the first CNN is input to the first group of layers. The second CNN may include the first group of layers in series with a third group of layers and be configured such that data for the second CNN is also input to the first group of layers.

Abstract: A combination video surveillance system and physical deterrent device is disclosed. At least one camera module of the video surveillance system defines a first field of view and is operable to generate image data corresponding to the first field of view. A video analytics module is configured to detect a foreground visual object falling within the first field of view, classify the visual object, and determine an appearance of the visual object. A positioning module is configured to determine a physical location of the visual object. A deterrence device controller is configured to receive the determined physical location of the visual object, to control a deterrence device to be aimed at the physical location of the visual object, and to control the deterrence device to selectively emit the physical effect.

Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of detected objects, each of which has a signature representing features of the detected object. The match classifier matches object instances by analyzing data derived from the signatures. The match classifier determines whether the signatures match.

Abstract: A video surveillance system having a plurality of aerial camera devices such as cameras on drones operable from a plurality of docking stations. Each station having a dock for receiving, charging, and controlling the aerial camera devices.

Abstract: A camera device includes a camera module that captures a scene corresponding to a field of view of the camera module. A wireless power transmitter of the camera devices transmits wireless power to a battery-powered external sensing device. A transceiver is further operable for receiving from the external sensing device sensed data. A network module transmits the image and sensed data to an external networked device. A power supply receives power over a wired connection and supplies power to the camera module, transceiver, networking module and wireless transmitter. The external sensing device may be an access control device that includes a sensor for sensing the presence of a machine readable toke and reading an identifier of the physical token. The access control device further includes a lock controller for selectively locking and unlocking a physical lock.

Abstract: A method, system and computer program product for interactively identifying same individuals or objects present in video recordings is disclosed. When a thumbnail in a set of thumbnails is selected, new information is obtained. The new information may be that an individual or object is present in the portion of the video recording associated with the thumbnail. A search can be carried out for the individual or object based on the new information. The search generates new match likelihoods for each of displayed thumbnails within a user interface page. The displayed thumbnails are re-ordered based on the new match likelihoods.