A system and method for processing alarms includes receiving alarm data from a third-party data source, the alarm data having visual data, an area of interest, and a sought target. The system processes the visual data to detect an object in the area of interest, and then classifies the object either in conformance with the sought target or in nonconformance with the sought target. The system issues a positive alarm when the object is in conformance with the sought target, and issuing issues a false alarm when the object is in nonconformance with the sought target. Feedback is received from the third-party data source regarding an accuracy of the respective positive alarm and the false alarm.

A monitoring device and an operation method thereof are provided to detect whether an object of interest appears in a video stream. The monitoring device includes a motion calculation circuit, a motion region determination circuit and a computing engine. The motion calculation circuit performs motion calculation on a current frame in the video stream to generate a motion map. The motion region determination circuit determines a motion region in the current frame according to the motion map. The motion region determination circuit notifies the computing engine with the motion region in the current frame. The computing engine performs an object of interest detection on the motion region in the current frame of the video stream to generate a detection result. The motion region determination circuit determines whether to ignore the motion region in a subsequent frame after the current frame according to the detection result.

A system and method for determining an object is disclosed. A security appliance, with a processor and memory is provided. A plurality of security devices are deployed within a defined neighborhood. The security appliance is configured to receive image of an object captured by a security device. The image of the object is processed to generate a first plurality of attributes for the object. The object is associated as belonging to the defined neighborhood.

A battery powered artificial intelligence autonomous patrol vehicle. The vehicle is battery powered and is operated autonomously using artificial intelligence. The autonomous patrol vehicle includes a surveillance or detection system on or within an emergency light bar of the autonomous patrol vehicle. The autonomous patrol vehicle autonomously performs patrol surveillance or detection functions such as speed; red-light; and traffic enforcement; traffic enforcement with Automatic License Plate Recognition (ALPR), face recognition, animal recognition, object recognition, video surveillance and online continuous analysis and any equivalent surveillance or detection functions. The autonomous patrol vehicle stores and sends live and/or stored data to a command control center while moving or parked. The surveillance or detection system navigates with the use of any one of or a combination of a camera, radar and Light Detection and Ranging (LIDAR) sensors and can operate by viewing 360 degrees.

Provided is an apparatus including a receiving unit for receiving an image captured by a monitoring camera; a determination unit for determining, based on the image that was received, the presence or absence of an occurrence of an event that interferes with monitoring by means of the monitoring camera; and a notification unit for notifying an administrator of the monitoring camera that the presence of the occurrence of the event has been determined, in response to the determination.

In a number of aspects, the invention discloses a device (100) comprising one or more infrared light sources (40, 41); one or more visible light sources (50, 51); an image sensor (10); a processing unit (20) configured to analyze a series of images of a region of interest output by the image sensor; a control unit (30) configured to generate one or more of an activation of the one or more visible light sources or an alarm based on a command received from the processing unit; wherein the analyze comprises detecting a moving foreground in the series of images of the region of interest, tracking one or more characterizing features in the moving foreground and classifying the one or more characterizing features into two of more types of objects of interest, a type of an object of interest determining a command sent by the processing unit to the control unit.

A monitoring system that is configured to monitor a property is disclosed. The monitoring system includes a passive infrared (PIR) sensor configured to generate reference PIR data that represents motion within an area of the property; an auxiliary sensor configured to generate auxiliary sensor data that represents an attribute of the area of the property; and a motion sensor device. The motion sensor device is configured to: obtain the reference PIR data; determine that a first set of motion detection criteria is satisfied by the reference PIR data; in response to determining that the first set of motion detection criteria is satisfied by the reference PIR data, obtain the auxiliary sensor data; obtain a second set of motion detection criteria based on the reference PIR data and the auxiliary sensor data; and determine whether the second set of motion detection criteria is satisfied by additional PIR data.

A system for situational awareness monitoring within an environment, wherein the system includes one or more processing devices configured to receive an image stream including a plurality of captured images from each of a plurality of imaging devices, the plurality of imaging devices being configured to capture images of objects within the environment and at least some of the imaging devices being positioned within the environment to have at least partially overlapping fields of view, identify overlapping images in the different image streams, the overlapping images being images captured by imaging devices having overlapping fields of view, analyse the overlapping images to determine object locations within the environment, analyse changes in the object locations over time to determine object movements within the environment, compare the object movements to situational awareness rules and use results of the comparison to identify situational awareness events.

Methods, and systems including computer programs encoded on a computer storage medium, for training a detection model for surveillance devices using semi-supervised learning. In one aspect, the methods include receiving imaging data collected by a camera of a scene within a field of view of the camera. Annotated training data is generated from the imaging data and one or more detection models are trained using the annotated training data. Based on a set of performance parameters, an optimized detection model is selected of the one or more detection models, and the optimized detection model is provided to the camera.

Systems and methods of selecting a video stream resolution are provided. In one exemplary embodiment, a method comprises, by a network node operationally coupled over a network to a set of optical sensor devices positioned throughout a space that are operable to send at least one of a set of image streams to the network node. The method comprises receiving a first image stream of a set of image streams of the first optical sensor device that is selected based on both a confidence level that at least one object is correctly detected from a second image stream received from the first optical sensor and a current network bandwidth utilization to maintain the current network bandwidth utilization below a network bandwidth utilization threshold, with the first and second image streams having a different resolution and the first optical sensor having a viewing angle towards the detected object.