System and method for video episode viewing and mining

A system configured to automatically analyze video data to identify movement patterns, the system comprising:

• a processing system comprising one or more processors;
• and
• a memory system comprising one or more computer-readable media, wherein the computer-readable media contain instructions stored thereon that, when executed by the processing system, cause the system to perform operations comprising:
• receiving, from one or more cameras, video data comprising a plurality of frames representing images of one or more objects within a physical area, wherein:
• the physical area is associated with a grid, the grid comprising a plurality of grid cells;
• and
• each camera of the one or more cameras is configured to capture video of one or more grid cells of the grid;
• identifying a plurality of events within the video data, wherein an event represents a movement of an object of interest from a first grid cell of the grid associated with the physical area to a second grid cell of the grid;
• generating a plurality of event data records reflecting the plurality of events;
• and
• determining one or more frequent episodes from the plurality of event data records, wherein an episode comprises a series of events associated with a particular object of interest.

A computer-implemented method of automatically analyzing video data to identify movement patterns, the method comprising:

• receiving, from one or more cameras, video data comprising a plurality of frames representing images of one or more objects within a physical area, wherein:
• the physical area is associated with a grid, the grid comprising a plurality of grid cells;
• and
• each camera of the one or more cameras is configured to capture video of one or more grid cells of the grid;
• identifying a plurality of events, wherein identifying an event comprises:
• identifying a first object of interest in a first frame;
• identifying, within a first grid cell of the grid, a first grid position of the first object of interest in the first frame;
• identifying a second object of interest in a second frame, wherein the second frame is adjacent to the first frame;
• identifying, within a second grid cell of the grid, a second grid position of the second object of interest in the second frame;
• determining that the second object of interest matches the first object of interest;
• determining that the second grid position differs from the first grid position, based on identifying that the first grid position is within the first grid cell and that the second grid position is within the second grid cell;
• and
• generating an event data record reflecting the first grid position and the second grid position of a monitored object of interest, wherein the monitored object of interest corresponds to the first object of interest and the second object of interest;
• and
• determining one or more frequent episodes from the generated event data records, wherein an episode comprises a series of events associated with a monitored object of interest.