The present disclosure discloses an object detection method used in an object detection apparatus that includes the steps outlined below. An image signal received from an image sensor is detected to generate an image detection signal when an image variation is detected. An infrared signal received from an infrared sensor is detected to generate an infrared detection signal when an infrared energy variation is detected. A time counting process is initialized when the image detection signal is generated. An object detection signal is generated when the infrared detection signal is generated within a predetermined time period after the time counting process is initialized. A detection distance of the image sensor is larger than a detection distance of the infrared sensor.

A proximity alarm assembly includes a personal electronic device that has wireless communication capabilities. A camera unit has a plurality of cameras facilitate 360.0 degree video surveillance of an area. A motion unit has a plurality of sensors to facilitate 360.0 motion surveillance of the area. Each of the motion unit and the camera unit is in wireless communication with the personal electronic device. Moreover, the personal electronic device produces an alert when motion unit senses motion to alert a user to the motion. An alert unit is positionable near the user and the alert unit is actuated to emit an audible alert and a visual alert when either of the camera unit or the motion unit fails to communicate with the personal electronic device.

In one implementation, a method for using a mobile computing device as a security system component includes receiving, from a mobile device and by a security system gateway, mobile device sensor information associated with sensor data captured by the mobile device at a premises monitored by the security system gateway; identifying, based on the received sensor information, a mobile device sensor information rule; identifying an action to perform in the mobile device sensor information rule; and automatically performing the identified action, by the security system gateway, in response to receiving the sensor information from the mobile device.

Disclosed are methods, systems, and apparatus for adjusting areas of interest for motion detection in camera scenes. A method includes obtaining a map of false motion event detections using a first area of interest; identifying an overlap area between the map of false detections and the first area of interest; determining a second area of interest that includes portions of the first area of interest and excludes at least a part of the overlap area; obtaining a map of true motion event detections using the first area of interest; determining whether true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria; and in response to determining that true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria, providing the second area of interest for use in detecting events.

Provided is a data stream allocation system capable of suppressing inequality in processing loads on a plurality of servers and delays in analysis processing at the servers when the servers analyze the objects of analysis using data streams obtained from a plurality of camera systems. The allocation determination unit 4 allocates data streams obtained from a plurality of camera systems to a plurality of servers according to the unevenness in the number of objects in the surveillance region. Then, the distribution unit 5, in accordance with the results of the allocation of the data streams to the plurality of servers, distributes each of the data streams obtained from the camera systems to the server to which the data stream has been allocated.

A method for monitoring and securing a facility without employing a manned control center, according to which a server acquires surveillance data from each of a plurality of distributed surveillance devices deployed within or adjacent to a given security sensitive facility. Then the server receives an intrusion alert from a guard, or from one or more of the plurality of surveillance devices to initiate a triggering event, if the acquired surveillance data is indicative of activity that has been detected at an indicated location. A first guard is alerted and presented with a full situational awareness image to assess the severity of the detected activity and the triggering event is terminated if found by the first guard to be of a low severity, or an interception operation initiated by self-dispatching or dispatching one or more additional guards to a close by location that holds tactical advantage relative to the indicated location if the triggering event was found by the first guard to be of a high severity.

Aspects of the present disclosure are directed to, for example, a method for object detection, recognition, classification and tracking using a distributed networked architecture. In some embodiments, the distributed network architecture may include one or more sensor units wherein the image acquisition and the initial feature extraction are performed and a gateway processor for further data processing. Some aspects of the present disclosure are also directed to a vision system for object detection, and to algorithms implemented in the vision system for executing the method acts for object detection, recognition, classification and/or tracking.

An image processing method including: capturing changes in a monitored scene; and performing a sparse feature calculation on the changes in the monitored scene to obtain a sparse feature map.

An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device. The processor may be configured to generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, determine whether the classification of the objects corresponds to a user-defined event and a user-defined identity and generate encoded video frames from the video frames. The encoded video frames may comprise a first sample of the video frames selected at a first rate when the user-defined event is not detected and a second sample of the video frames selected at a second rate while the user-defined event is detected. The video frames comprising the user-defined identity without a second person may be excluded from the encoded video frames.

A method includes receiving a first time series of video frames depicting a first area of interest that includes a display area in a retail environment, and using classification by a convolutional neural network to detect instances of people picking up inventory items from the display area. The method also includes receiving a second time series of video frames depicting a second area of interest in the retail environment, and determining, based upon one or more portions of the second time series of video frames, that at least one inventory item picked up at the display area was not, or will not likely be, checked out. The method also includes causing one or more alert messages to be displayed, including causing an alert message to be displayed based on the determining.