Visualizing and updating long-term memory percepts in a video surveillance system

A computer-implemented method for a video surveillance system to process a sequence of video frames depicting a scene captured by a video camera, comprising:

• receiving a request to view a visual representation of a percept encoded in a long-term memory of a machine-learning engine, wherein the percept encodes a pattern of behavior learned by the machine-learning engine from analyzing data streams generated from the sequence of video frames;
• retrieving the requested percept from the long-term memory of the machine-learning engine, wherein the long-term memory stores a plurality of percepts;
• generating a visual representation of the requested percept, wherein the visual representation presents a directed graph representing the pattern of behavior encoded by the requested percept;
• and
• receiving user input for a metadata attribute of the requested percept, wherein the metadata attribute specifies to not publish an alert message upon detecting, from the data streams, an observation of the learned pattern of behavior corresponding to the requested percept.

A non-transitory computer-readable storage medium containing a program which, when executed by a video surveillance system, performs an operation to process a sequence of video frames depicting a scene captured by a video camera, the operation comprising:

• receiving a request to view a visual representation of a percept encoded in a long-term memory of a machine-learning engine, wherein the percept encodes a pattern of behavior learned by the machine-learning engine from analyzing data streams generated from the sequence of video frames;
• retrieving the requested percept from the long-term memory of the machine-learning engine, wherein the long-term memory stores a plurality of percepts;
• and
• generating a visual representation of the requested percept, wherein the visual representation presents a directed graph representing the pattern of behavior encoded by the requested percept;
• and
• receiving user input for a metadata attribute of the requested percept, wherein the metadata attribute specifies to publish an alert message upon detecting, from the data streams, an observation of the learned pattern of behavior corresponding to the requested percept.

A non-transitory computer-readable storage medium containing a program which, when executed by a video surveillance system, performs an operation to process a sequence of video frames depicting a scene captured by a video camera, the operation comprising:

• receiving a request to view a visual representation of a percept encoded in a long-term memory of a machine-learning engine, wherein the percept encodes a pattern of behavior learned by the machine-learning engine from analyzing data streams generated from the sequence of video frames;
• retrieving the requested percept from the long-term memory of the machine-learning engine, wherein the long-term memory stores a plurality of percepts;
• generating a visual representation of the requested percept, wherein the visual representation presents a directed graph representing the pattern of behavior encoded by the requested percept;
• and
• receiving user input for a metadata attribute of the requested percept, wherein the metadata attribute specifies to not publish an alert message upon detecting, from the data streams, an observation of the learned pattern of behavior corresponding to the requested percept.

A video surveillance system, comprising:

• a video input source configured to provide a sequence of video frames, each depicting a scene;
• a processor;
• and
• a memory containing a program, which when executed by the processor is configured to perform an operation to process the scene depicted in the sequence of video frames, the operation comprising:
• receiving a request to view a visual representation of a percept encoded in a long-term memory of a machine-learning engine, wherein the percept encodes a pattern of behavior learned by the machine-learning engine from analyzing data streams generated from the sequence of video frames,
• retrieving the requested percept from the long-term memory of the machine-learning engine, wherein the long-term memory stores a plurality of percepts, and
• generating a visual representation of the requested percept, wherein the visual representation presents a directed graph representing the pattern of behavior encoded by the requested percept, and
• receiving user input for a metadata attribute of the requested percept, wherein the metadata attribute specifies to publish an alert message upon detecting, from the data streams, an observation of the learned pattern of behavior corresponding to the requested percept.

A video surveillance system, comprising:

• a video input source configured to provide a sequence of video frames, each depicting a scene;
• a processor;
• and
• a memory containing a program, which when executed by the processor is configured to perform an operation to process the scene depicted in the sequence of video frames, the operation comprising:
• receiving a request to view a visual representation of a percept encoded in a long-term memory of a machine-learning engine, wherein the percept encodes a pattern of behavior learned by the machine-learning engine from analyzing data streams generated from the sequence of video frames,
• retrieving the requested percept from the long-term memory of the machine-learning engine, wherein the long-term memory stores a plurality of percepts,
• generating a visual representation of the requested percept, wherein the visual representation presents a directed graph representing the pattern of behavior encoded by the requested percept, and
• receiving user input for a metadata attribute of the requested percept, wherein the metadata attribute specifies to not publish an alert message upon detecting, from the data streams, an observation of the learned pattern of behavior corresponding to the requested percept.