A security system can use video analytics and/or other input parameters to identify a theft event. Optionally, the security system can take remedial action in response. For example, the security system can use video analytics to determine that a person has reached into a shelf multiple times at a rate above a threshold, which can indicate that a thief is quickly removing items from the shelf. The security system can also use video analytics to determine that a person has reached into a shelf via a sweeping action, which can indicate that a thief is gathering and removing a large quantity of items from the shelf in one motion. In response, the security system can alert security personnel, cause a speaker to output an audible message in the target area, flag portions of the video relating to the theft event, activate or ready other sensors or systems, and/or the like.

An image outputting apparatus comprises: a first accepting unit which accepts an output instruction of a captured image; and an outputting unit which outputs, in a case where, in the captured image captured at a first time point, a predetermined event occurs and the output instruction is accepted at a second time point after the occurrence of the predetermined event and the predetermined event is continuing at the second time point, the captured image captured by an imaging unit at and after the second time point, and outputs, in a case where the output instruction is accepted at the second time point and the predetermined event does not continue at the second time point, the captured image captured during the continuation of the predetermined event.

The present invention relates to a method and apparatus for processing video image data, so as to apply different types of processing to different aspects of video image data. A detection process is arranged to detect a item, object or event appearing or occurring in a scene being viewed by an image device. An image data process is responsive to the detection of the object or event and to control information to process the image data for a portion of the scene where the object or event appears or occurs, differently from the processing of the image data associated with the rest of scene. For example, the object may be a person's face, and the face image data may be processed to produce high resolution data, the rest of the scene being provided in low resolution. This saves on processing, transmission and storage.

Systems and methods for increasing an efficiency of an operator of a security system are discussed generally herein. A system can include a memory including ontology data saved thereon, the ontology data can define interrelationships between a scanner associated with access to a room of an area under surveillance, a camera with a field of view at least partially overlapping a footprint of the room, an identifier configured to be scanned by the scanner and associated with a person, and a security policy including one or more predefined conditions, which when satisfied, indicate when a security threat exists, the security policy includes a response an operator can perform if the conditions are satisfied, and the system can include a query module configured to receive a query and search the ontology data and temporal and spatial data associated with the area under surveillance in response to receiving the query.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for preventing false alarms due to display images. In one aspect, a monitoring system is disclosed that includes a processor and a computer storage media storing instructions that, when executed by the processor, cause the processor to perform operations. The operations can include obtaining, by the monitoring system, image data that depicts a portion of a property, determining, by the monitoring system, that the image data depicts an object, based on determining, by the monitoring system, that the image data depicts an object, determining, by the monitoring system, whether the depicted object is located within an exclusionary region of the property, and based on determining, by the monitoring system, that the depicted object is not located within an exclusionary region of the property, triggering, by the monitoring system, an event based on the image data.

A task circumstance processing system includes a processor that executes a process. The process includes: referencing a recognition information stored in a memory, the recognition information stores, for each of plural task processes in task definitions defining relationships between the plural task processes, recognition information for recognizing execution of each of the plural task processes, and extracting for each of the task processes a timing where the recognition information is expressed in observation data from observing circumstances of the task; and outputting a result of comparing a relationship between plural task processes that have been executed as identified by the extracted timings, against a relationship between plural task processes defined by the task definitions stored in the memory.

A method and system for monitoring a retail environment by performing video content analysis based on two-dimensional image data and depth data are disclosed. Accuracy in customer actions to provide assistance, change marketing behavior, safety and theft, for example, is increase by analyzing video containing two-dimensional image data and associated depth data. Height data may be obtained from depth data to assist in object detection, object classification (e.g., detection a customer or inventory) and/or event detection.

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.

The various embodiments described herein include methods, devices, and systems for categorizing motion events. In one aspect, a method is performed at a camera device. The method includes: (1) capturing a plurality of video frames via the image sensor, the plurality of video frames corresponding to a scene in a field of view of the camera; (2) sending the video frames to the remote server system in real-time; (3) while sending the video frames to the remote server system in real-time: (a) determining that motion has occurred within the scene; (b) in response to determining that motion has occurred within the scene, characterizing the motion as a motion event; and (c) generating motion event metadata for the motion event; and (4) sending the generated motion event metadata to the remote server system concurrently with the video frames.

An anti-theft method and apparatus relating to the field of anti-theft technologies, and to reduce costs. The anti-theft method includes obtaining a detection signal from a terminal device, where the detection signal is a signal sent by the terminal device when the terminal device detects an available network, and performs an alarm operation when the detection signal meets a preset condition. The anti-theft method may be applied in daily life in order to ensure family property security.