Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: An image of at least a portion of a room may be received, the image of the room comprising an image of a sensor mounted in the room. At least one optical parameter related to the image of the room may also be received. A distance may be determined between the sensor and a camera that captured the image of the room, wherein the determination of the distance is based at least in part on the optical parameters and on known physical dimensions of the sensor. A sensitivity requirement of the sensor may be determined, based on the distance. The determined sensitivity may be sent to control logic of the sensor.

Abstract: Systems and techniques are provided for sensor bypass. Activation may be received at a bypass input of an entry point sensor of a security system while the entry point sensor is in an armed mode. The entry point sensor may detect that the entry point monitored by the entry point sensor is closed. The entry point sensor may enter into a bypass mode. Detection by the entry point sensor of an opening of the entry point while the entry point sensor is in the bypass mode may not result in the generation of an alarm by the security system.

Abstract: In a method for detection of an anomaly related to information about a location of a mobile computing device, the anomaly related to the information about the location of the mobile computing device can be detected by a processor of the mobile computing device. A communication about the anomaly can be provided via a user interface of the mobile computing device. The anomaly can be caused by a bug in an operating system of the mobile computing device. Notifications about the anomaly can be provided to a user of the mobile computing device or other mobile computing devices registered to a shared account.

Abstract: Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A system and method for detecting human intruders while rejecting/ignoring an occupant's registered pet. An object detection system is configured to detect an object that is present in a monitored area and generate a signal output relative to the type of object. A signature processor is configured to receive the generated signal output and produce an object signature, and compare a threshold signature to the object signature, wherein the threshold signature is generated using a photograph of a reserved object, and wherein the object detection system rejects the detected object when the object signature is determined to be similar to the threshold signatures.

Abstract: An electronic device may that communicates with a plurality of electronic devices may include a network interface and a processor. The network interface may receive a first set of data from a first electronic device and a second set of data from a second electronic device. The processor may then determine a first trust value for the first set of data based on a first corroboration of the first set of data using the second set of data. The first corroboration may include using the second set of data to verify the first set of data.

Abstract: This patent specification relates to apparatus, systems, methods, and related computer program products for providing home security objectives. More particularly, this patent specification relates to a plurality of devices, including intelligent, multi-sensing, network-connected devices, that communicate with each other and/or with a central server or a cloud-computing system to provide any of a variety of useful home security objectives.

Abstract: A method includes capturing data with a plurality of network connected sensors installed in or around a premises, storing at least a sampling of the data in an electronic storage device, analyzing the stored data with a processor to automatically identify one or more types of trends or patterns, creating, based on the analysis, a zone definition for a first zone that corresponds to an area of the premises from which data was captured by one or more sensors selected from among the plurality of sensors, and configuring at least one system operating in or around the premises automatically based on the first zone definition.

Abstract: A magnet and magnetometer may be integrated into a smart home environment and allow it to be placed into an away mode of operation despite an entry point being semi-open. The disclosed implementations can detect a magnetic field strength and determine, based on the detected field strength, an approximate distance that a moveable partition is open. In some configurations, the presence of a second magnetic source can be detected. A notice may be generated based on one or more signals received from the magnetometer. The notice may be sent to a controller, a remote system, a remote device, and/or a client device as disclosed herein.

Abstract: Systems and techniques are provided for learned overrides for home security. A sensor of a security system may be armed. A trip signal may be received indicating a tripping of the sensor. It may be determined that the trip signal can be automatically overridden based on matching an identity of the sensor and a state of the security system with a pattern in a model. The pattern may represent a state of the security system in which automatically overriding the trip signal from the sensor is permitted. The trip signal from the sensor may be automatically overridden without input from a user.

Abstract: Systems and techniques are provided for automatic security system mode selection. A set of signals may be received from sensors distributed in an environment with a security system. The security system may be in a first mode. An occupancy model may be received. An occupancy estimate may be generated for the environment based on the set of signals from the sensors and the occupancy model. Mode rules may be received. The mode rules associate occupancy estimates with modes of the security system. A second mode for the security system may be determined based on the occupancy estimate and mode rules. The second mode may be different from the first mode. The mode of the security system may be automatically changed from the first mode to the second mode.

Abstract: A method of controlling a security system of a premises includes detecting one or more exceptions when the system is set to an alarm mode, determining whether any of the one or more exceptions is a terminal exception, automatically executing an arming procedure according to the alarm mode when all of the exceptions are determined to be non-terminal exceptions, preventing execution of the arming procedure when any of the exceptions are determined to be a terminal exception, and, while in the alarm mode, preventing a sensor associated with a security exception from triggering an alarm when the security exception is fully corrected, and triggering an alarm when a condition that is causing the security exception is adjusted without resulting in full correction of the security exception.

Abstract: Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Abstract: In a method for detection of an anomaly related to information about a location of a mobile computing device, the anomaly related to the information about the location of the mobile computing device can be detected by a processor of the mobile computing device. A communication about the anomaly can be provided via a user interface of the mobile computing device. The anomaly can be caused by a bug in an operating system of the mobile computing device. Notifications about the anomaly can be provided to a user of the mobile computing device or other mobile computing devices registered to a shared account.

Abstract: Systems and techniques are provided for security system re-arming. Input invoking restricted credentials may be received. The security system of an environment may be changed from a first mode to a second mode based on the restricted credentials. The restricted credentials used to change the security system to the second mode may be determined to be near expiration based on an expiration condition of the restricted credentials. A notification may be sent to a person associated with the restricted credentials including a reminder to use the restricted credentials to change the security system to the first mode before the restricted credentials expire.