An imaging disturbance detection device includes an estimation information obtaining unit that obtains pieces of disturbance estimation information indicating whether or not there is imaging disturbance on each of a plurality of imaging devices installed on a railroad car, which is estimated by comparing luminance of images taken by the imaging devices with predetermined reference luminance set; and a disturbance identifying unit that identifies, when it is estimated, on the basis of the obtained pieces of disturbance estimation information, that there is imaging disturbance on a first imaging device, whether or not there is imaging disturbance on a first imaging device on the basis of which one, presence or absence of imaging disturbance, is estimated for a second imaging device, and a length of a period during which it is continuously estimated that there is imaging disturbance on the first imaging device.

An unmanned aerial vehicle is described and includes a computer carried by the unmanned aerial vehicle to control flight of the unmanned aerial vehicle and at least one sensor. The unmanned aerial vehicle is caused to fly a specific pattern within a facility, receive sensor data from a sensor carried by the vehicle, apply processing to the sensor data to detect an unacceptable level of detected feature differences in features contained in the sensor data, determine a new flight instruction for the vehicle based on the processing; and send the new flight instruction for the vehicle to a system for controlling flight of the vehicle.

There is provided with an image processing apparatus. A target image including an object, for a predetermined imaging range, is obtained. A background image, which is obtained from a selected image selected from a captured image for the predetermined imaging range based on brightness of the target image, is obtained. A region of the object in the target image based on the background image is obtained.

Various embodiments concern video patient monitoring with detection zones. Various embodiments can comprise a camera, a user interface, and a computing system. The computing system can be configured to perform various steps based on reception of a frame from the camera, including: calculate a background luminance of the frame; monitor for a luminance change of a zone as compared to one or more previous frames, the luminance change indicative of patient motion in the zone; and compare the background luminance to an aggregate background luminance, the aggregate background luminance based on the plurality of frames. If the background luminance changed by more than a predetermined amount, then the aggregate background luminance can be set to the background luminance, luminance information of the previous frames can be disregarded, and motion detection can be disregarded.

A method may include transmitting a video stream of a live scene over a network at a real-time transmission speed and detecting an event associated with the video stream being transmitted. The method may include transmitting the video stream over the network at a speed lower than the real-time transmission speed during the event. Transmitting the video stream at the speed lower than the real-time transmission speed may introduce a time stretch for the video stream to be played in slow motion. The method may include reducing a bitrate of the video stream after the event and transmitting the video stream with the reduced bitrate over the network after the event to compensate for the time stretch.

A system, method and apparatus for configuring a person detection sensor. The person detection sensor may limit its transmissions in accordance with a pre-configured dwell time. The person detection sensor may receive a new dwell time from a personal communication device. When the new dwell time is received, it is stored in memory and is then used to regulate the number of transmissions of the person detection sensor in accordance with the new dwell time.

The present application describes a system and method for extending a range of an image detection and classification system that is associated with various image capture devices. The range of the image detection and classification system is extended using one or more of an optical zoom on an area of interest, a digital zoom on the area of interest and a crop operation on the area of interest.

The present application describes a system and method for mitigating false alarms caused by repeated and/or sporadic movement of objects in a field of view of an image capture device.

The present application describes a video processing request system that receives video files and synchronous task requests, converts the synchronous task requests into asynchronous task requests, provides the video files to various processing units in response to the conversion, and subsequently provides processed video files back to requesting computing devices in response to the received synchronous task requests.

Various embodiments concern video patient monitoring with detection zones. Various embodiments can comprise a camera, a user interface, and a computing system. The computing system can be configured to perform various steps based on reception of a frame from the camera, including: calculate a background luminance of the frame; monitor for a luminance change of a zone as compared to one or more previous frames, the luminance change indicative of patient motion in the zone; and compare the background luminance to an aggregate background luminance, the aggregate background luminance based on the plurality of frames. If the background luminance changed by more than a predetermined amount, then the aggregate background luminance can be set to the background luminance, luminance information of the previous frames can be disregarded, and motion detection can be disregarded.