Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: Apparatus and associated methods relate to training a neural network on a first host system, sending the neural network to a second host system, training the neural network by the second host system based on data private to the second host system, and employing the neural network to filter events sent to the first host system. In an illustrative example, the first host system may be a server having a central repository including trained neural networks and historical data, and the second host system may be a remote server having a data source private to the remote server. The private remote data source may be a camera. In some examples, events may be filtered as a function of a prediction of error in the neural network. Various examples may advantageously provide remote intelligent filtering. For example, remote data may remain private while adaptively filtering events to the central server.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.