An integrated security management system is provided. The system includes an application server and a plurality of sensors deployed in a geographical area. The application server receives first sensor data from the plurality of sensors and provide to a trained classification model as input and detects a security alert based on output thereof. The application server determines a patrol route that encompasses a location of security alert and transmits a surveillance request to electronic device of a security operator to patrol the patrol route and identifies one or more sensors that covers the location of the security alert and receives second sensor data therefrom based on location of the electronic device being same as location of the security alert. The application server further re-trains the classification model based on the second sensor data when feedback received from the electronic device indicates the security alert to be a false positive.

A method for eliminating sensor errors, in particular ambiguities when detecting dynamic objects, by a control device, is provide. Measurement data are received from at least one first sensor and object hypotheses are formed from the received measurement data. Data of at least one reference object, which is detected based on measurement data from at least one second sensor, are received. The formed object hypotheses are compared with the at least one detected reference object. Object hypotheses that do not match the detected reference object are rejected. A method for eliminating sensor errors, in particular ambiguities when detecting static objects is also provided.

A vehicle mounted liveness detection system, liveness detection method, apparatus, computer device and storage medium are disclosed. The system includes a Doppler microwave sensor, a camera and an alarm controller. The Doppler microwave sensor is configured to perform a liveness search on an in-vehicle space, obtain a liveness search data, and transmit the liveness search data to the alarm controller. The camera is configured to capture an in-vehicle image of the in-vehicle space and transmit the in-vehicle image to the alarm controller. The alarm controller is configured to perform a liveness detection on the liveness search data, perform a liveness detection on the in-vehicle image and perform an in-vehicle liveness alarm operation when a presence of liveness in the in-vehicle space is detected based on at least one of the liveness search data and the in-vehicle image.

A system for detecting a presence in an environment to be monitored includes an electrostatic charge variation sensor, a vibration sensor, and an environmental pressure sensor. A processing unit is configured to acquire, from the electrostatic charge variation sensor, an electrostatic charge variation signal, and detect in the electrostatic charge variation signal, first signal characteristics indicative of the presence of a subject in the environment to be monitored. The processing unit further validates the detection of presence of the subject using the vibration and pressure signals provided by the other sensors.

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.

A security sensor is provided which has a container body and a signal acquisition and processing module housed in the container body. The signal acquisition and processing module has a piezoelectric transducer configured to convert a mechanical stress to which the piezoelectric transducer is subjected into a first electrical signal, an accelerometric transducer configured to convert an acceleration to which the accelerometric transducer is subjected into a second electrical signal, and processing unit operatively connected to the piezoelectric transducer and to the accelerometric transducer for receiving the first and second electrical signals. The accelerometric transducer is a MEMS accelerometric transducer.

Among other things, techniques are described for preventing and mitigating fire risks on a vehicle. For example, multiple sensor inputs are provided to a predictive model which determines fire scenarios. A first fire risk data and second fire risk data are received from the sensors, and data indicative of fire prevention and fire mitigation is received. At least one fire prevention measure applicable to the first imminent fire risk condition and the second imminent fire risk condition is determined. The first fire risk data and the second fire risk data are compared to the fire mitigation thresholds. At least one fire mitigation measure applicable to the first imminent fire risk condition and the second imminent fire risk condition is determined. The at least one fire prevention measure or the at least one fire mitigation measure is activated.

One variation of a method for detecting a fire includes: during a first time period: detecting an increase in ambient light intensity and detecting an increase in ambient humidity; responsive to the increase in ambient light intensity and the increase in ambient humidity, detecting a fire event; during a second time period: correlating a decrease in ambient light intensity with an increase in visual obscuration; detecting an increase in ambient air temperature; in response to a magnitude of the increase in visual obscuration remaining below a high obscuration threshold and a magnitude of the increase in ambient temperature remaining below a high temperature threshold, classifying the fire as an incipient fire; and, in response to the magnitude of the increase in visual obscuration exceeding the high obscuration threshold and the magnitude of the increase in ambient temperature exceeding the high temperature threshold, classifying the fire as a developed fire.

An electronic monitoring and protection system which obtains permission from each of a plurality of users to access security data from one or more monitoring devices. The electronic monitoring and protection system using the security data to determine a threat level posed to a person to be protected, and initiating an action if the threat level exceeds a predetermined threshold.

A vehicle safety system is for preventing child abandonment in a vehicle. The vehicle safety system may include a controller coupled to a flow sensor, a temperature sensor, and a seat sensor. The controller may be configured to detect when the vehicle is in a fueling state based upon the flow sensor, detect occupancy of a child safety seat based upon the seat sensor, and detect when a driver side door is in an open state based upon a driver side door sensor in the vehicle. The controller may be configured to when the driver side door has entered the open state, when the child safety seat is occupied, and when the vehicle does not enter the fueling state within a time period, then cause the vehicle to enter an alert state.