A method comprising: automatically processing recorded first sensor data from a scene to recognise automatically a first user input from user action in the scene; in response to recognition of the first user input, automatically entering a learning state to enable: automatic processing of the first sensor data from the scene to capture an ad-hoc sequence of spatial events in the scene subsequent to the first user input and automatic processing of subsequently recorded second sensor data from the scene different to the first sensor data of the scene, to recognise automatically a sequence of spatial events in the subsequently recorded second video corresponding to the captured sequence of spatial events.

The various implementations described herein include methods, devices, and systems for detecting motion and persons. In one aspect, a method is performed at a smart home system that includes a video camera, a server system, and a client device. The video camera captures video and audio, and wirelessly communicates, via the server system, the captured data to the client device. The server system: (1) receives and stores the captured data from the video camera; (2) determines whether an event has occurred, including detected motion; (3) in accordance with a determination that the event has occurred, identifies video and audio corresponding to the event; and (4) classifies the event. The client device receives information indicative of the identified events, displays a user interface for reviewing the video and audio stored by the remote server system, and displays the at least one classification for the event.

Provided herein are systems and methods for implementing a video surveillance system. In one or more examples, the improved system can include a video managing system that applies one or more watchlists, to video footage captured by surveillance cameras to apply one or more processing policies indicated by the one or more watchlists. In some embodiments, a watchlist may indicate a plurality of events of interest (e.g., specific classes of objects, a presence of one or more persons, or one or more persons of interest, etc.) that should be detected from video footage. Through these mechanisms, video processing can be increased since the types of events of interest that need to be detected within the video footage are reduced. Non-transitory storage requirements are reduced, and human labor requirements to monitor, analyze, and review video surveillance footage is reduced.

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes: (1) receiving a plurality of video frames from a camera, the plurality of video frames including a motion event candidate; (2) categorizing the motion event candidate by processing the plurality of video frames, the categorizing including: (a) associating the motion event candidate with a first category of a plurality of motion event categories; and (b) generating a confidence level for the association of the motion event candidate with the first category; and (3) sending an alert indicative of the first category and the confidence level to a user associated with the camera.

A system and method to facilitate trespass incident reporting. The system includes a camera, a processor, a communication device, a remote server, a storage device, and a user personal computing (PC) device. The processor aids in identifying a trespasser using the facial images acquired by the camera through a visual comparison with known trespassers. The processor sends the acquired facial images to the remote server through the communication device. Then the acquired facial images are matched with images of trespassers in a trespasser database stored on the storage device. When no match is found, the remote server prompts the user's PC device to enter information for the first-time trespasser, and to present an audio no-trespass warning message to the trespasser while concurrently recording the presentation using an audio/video recorder. With the no-trespass recording, a trespasser incident report is created and distributed to all users.

Customizable intrusion zones for audio/video (A/V) recording and communication devices in accordance with various embodiments of the present disclosure are provided. In one embodiment, a method for an A/V recording and communication device is provided, the method comprising displaying, on a display of the computing device, a user interface for creating and/or customizing at least one intrusion zone, wherein the at least one intrusion zone comprises at least one motion zone within a field of view of the A/V recording and communication device coupled with at least one conditional setting of the at least one motion zone, determining whether an input has been received to establish a new conditional setting, or to change a previous conditional setting, determining whether an input has been received to save the new conditional setting or to save the changed conditional setting, and saving the new conditional setting or the changed conditional setting.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement video surveillance with neural networks are disclosed. Example systems disclosed herein include a database to store records of operator-labeled video segments (e.g., as records of operator-labeled video segments). The operator-labeled video segments include reference video segments and corresponding reference event labels describing the video segments. Disclosed example systems also include a neural network including a first instance of an inference engine, and a training engine to train the first instance of the inference engine based on a training set of the operator-labeled video segments obtained from the database, the first instance of the inference engine to infer events from the operator-labeled video segments included in the training set. Disclosed example systems further include a second instance of the inference engine to infer events from monitored video feeds, the second instance of the inference engine being based on the first instance of the inference engine.

A method of performing movement pattern anomaly detection with targeted alerts can include receiving input from each camera of a multi-camera system and for each input: performing video content analysis; generating a critical analysis matrix associated with the input from that camera; assigning a fusion value for each vector of the critical analysis matrix using a fusion map that indicates particular fusion values associated with possible elements of the critical analysis matrix; and triggering an alert according to whether the fusion value exceeds a threshold associated with that camera. The critical analysis matrix includes output from at least two different computer vision algorithms of the video content analysis applied to the input from a camera.

A method for object monitoring of a rail vehicle using a video monitoring system includes capturing a measurement signal of a video camera and determining image data of an environment of a vehicle interior according to the captured measurement signal. A piece of luggage in the vehicle interior is identified according to the determined image data. The identified piece of luggage is personalized. A manipulation of the identified piece of luggage is determined according to a predefined manipulation condition. A notification signal is sent according to the determined manipulation and according to the personalizing of the identified piece of luggage. A device for operating a video monitoring system for a rail vehicle for object monitoring and a rail vehicle are also provided.

Some embodiments provide a method for separating the motion detection zone(s) of an A/V recording and communication device from the motion alert zone(s) of the A/V recording and communication device. For example, an A/V recording and communication device may be configured to generate motion alerts, and to record audio and video footage, when an event is detected within a selected motion alert zone (e.g., within a defined radius around the A/V recording and communication device). However, the A/V recording and communication device may not generate a motion alert for an event detected outside of the selected motion alert zone, even though the device may still record audio and video footage for the detected event (e.g., when the event is within a selected motion detection zone).