Security guards at big facilities, such as airports, monitor multiple screens that display images from individual surveillance cameras dispersed throughout the facility. If a guard zooms with a particular camera, he will lose image resolution, along with perspective on the surrounding area. Embodiments of the inventive Imaging System for Immersive Surveillance (ISIS) solve these problems by combining multiple cameras in one device. When properly mounted, example ISIS systems offer 360-degree, 100-megapixel views on a single screen. (Other resolutions may also be employed.) Image-stitching software merges multiple video feeds into one scene. The system also allows operators to tag and follow targets, and can monitor restricted areas and sound an alert when intruders breach them.

Example implementations include a method, apparatus, and computer-readable medium for sharing analytical resources in a camera network, comprising capturing a video clip comprising a set of image frames via a first camera having only non-artificial intelligence (A.I) features, wherein the first camera is part of a camera network. The implementations further include identifying, in the camera network, a second camera that has an A.I feature. The implementations further include determining whether the second camera has bandwidth to analyze the video clip. The implementations include transmitting, via the first camera, the video clip to the second camera for analysis using the A.I feature, in response to determining that the second camera has bandwidth. The implementations include receiving, by the first camera from the second camera, metadata comprising results of the analysis using the A.I feature and generating, for display on a user interface, the video clip with the metadata.

A server includes: a registration unit that registers identification information for identifying a detection target in a storage unit; a detection target determination unit that determines whether the detection target is shown in an image captured on or around a road, based on the identification information; an abnormal behavior determination unit that determines whether the detection target exhibits an abnormal behavior that is different from a normal behavior of the detection target, when the detection target is shown in the image; and an alert transmission unit that transmits an alert when the detection target exhibits the abnormal behavior.

A method, system and computer program product for emulating depth data of a three-dimensional camera device is disclosed. The method includes concurrently operating the radar device and the 3D camera device to generate training radar data and training depth data respectively. Each of the radar device and the 3D camera device has a respective field of view. The field of view of the radar device overlaps the field of view of the 3D camera device. The method also includes inputting the training radar and depth data to the neural network. The method also includes employing the training radar and depth data to train the neural network. Once trained, the neural network is configured to receive real radar data as input and to output substitute depth data.

A first imager has a relatively high resolution and a relatively narrow first field-of-view. Information about objects in an environment is detected or captured, and used to steer the first field-of-view of the first imager. The sensor(s) may take the form of a second imager with a relatively lower resolution and relatively wider second field-of-view. Alternatively, other types of sensors, for instance presence/absence sensors may be employed. The first field-of-view may be directed toward an object that satisfies one or more conditions, for instance matching a particular SKU. The first field-of-view may track a moving object, for instance via a tracking mirror and actuator. This approach may be employed in retail locations, for example in grocery or convenience stores, for instance to reduce various forms of theft or in industrial environments.

A surveillance system includes fixed video cameras and mobile security devices. Each mobile security device includes a mobile video camera, a memory, a transceiver and a controller that is configured to receive instructions from a surveillance system controller to fly to a particular location at which an incident is believed to be occurring and to instruct the mobile video camera to capture video of the incident. In some cases, the controller of the mobile security device is configured to solicit and receive one or more video streams from fixed video cameras that are determined to meet predetermined criteria with respect to the incident, and store the one or more received video streams in the memory. The controller of the mobile security device may subsequently transmit the collected video streams to the surveillance system controller.

A target object identification system includes a first camera, a second camera, and a processor. The first camera acquires an image of a first target region. The second camera synchronously acquires an image of a second target region. The second target region includes part or all of the first target region. Resolution of the first camera is higher than that of the second camera, and field of view of the second camera is greater than that of the first camera. The processor identifies first target objects according to the image of the first target region, and second target objects according to the image of the second target region, and determines association relationships between the first target objects in the image of the first target region and the second target object in the synchronously acquired image of the second target region.

An adaptive image acquisition system and method that generates virtual view of a surveillance scene to a user (operator), in which, the user operates the system. Through viewing the virtual view, the user controls sensors that create the virtual view. The sensors comprise at least one first sensor having a higher resolution than at least one second sensor. Images from the second sensor are processed to create an image mosaic that is overlaid with images from the higher resolution first sensor. In one embodiment of the invention, the first sensor is moved using Saccade motion. In another embodiment of the invention, a user's intent is used to control the Saccade motion.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving mission specific data. Converting the mission specific data into attribute classes recognizable by image recognition classifiers, where the image recognition classifiers are pre-trained to detect objects corresponding to the attributed classes within digital images. Obtaining, from a wearable camera system, low resolution images of a scene and high resolution images of the scene. Detecting, within the low resolution images using a low resolution image classifier, an object that corresponds to one of the attribute classes. In response to detecting the object that corresponds to one of the attribute classes, providing, for presentation to a user of the wearable camera system by a presentation system, an first alert indicating a potential detection of the suspect, and providing the high resolution images to a high resolution image classifier. Obtaining, from the high resolution image classifier, a confirmation of the detected object from the low resolution images. In response to obtaining the confirmation, providing, for presentation to the user by the presentation system, a second alert indicating confirmation that the object has been detected.

Techniques are described for an autonomous asset management system that integrates autonomous devices, such as drone devices and other robotic devices, with a home security system of a property to enable management, monitoring, and/or tracking of various assets located within the property. In some implementations, an indication of an asset associated with a property is obtained by an autonomous device. Sensor data collected by one or more sensors of the property is obtained by the autonomous device based on the indication of the asset. A present status of the asset is determined by the autonomous device based on the sensor data. A determination that the present status of the asset does not correspond to an expected status of the asset is made by the autonomous device. In response, the autonomous device navigates to the particular location of the property.