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: Automatically monitoring and deterring crime includes: monitoring by an artificial intelligence module one or more areas; determining by the artificial intelligence module to recommend one or more intervention measures from a plurality of possible intervention measures for the one or more areas; determining by the artificial intelligence module that approvals are needed from one or more designated approvers for use of the one or more recommended intervention measures; and seeking approval by the artificial intelligence module from the one or more designated approvers for use of the one or more recommended intervention measures.

Abstract: Provided herein are systems for crime detection, deterrence, and intervention and methods of use thereof. The crime detection, deterrence, and intervention system employs an artificial intelligence module to reliably monitor and detect when crimes are in progress. A request to deploy a deterrence or intervention measure is evaluated by the artificial intelligence module to determine whether the requested deterrence or intervention measure should be automatically approved, automatically denied, or sent for further review and approval.

Abstract: A method is disclosed. The method includes providing an imaging apparatus, recording image data of an imaging location using the imaging apparatus, displaying the image data to a user via a user device, selecting an image object from the image data based on a selection criteria, and determining whether or not a selection criteria error of the image object is to be checked. The method also includes displaying a bounding shape, which bounds the image object, to the user via the user device when the selection criteria error is to be checked, prompting the user to enter user input indicating whether or not the selection criteria error is present, and storing data of the image object in a cache when the user input indicates that the selection criteria error is present.

Abstract: A system and method for training an AI model. A recorded video is divided into video frames that are input and read by a processor that identifies objects in the video frames using the object's latent characteristics. The processor further classifies an event based on the identified object, the latent characteristics, and surrounding factors at the time the object is identified. Video frames are annotated based on the identified object and classified event. A user's responses to annotated frames are tracked and the latent characteristics are adjusted based on the user's responses.

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.