Predictive security camera system

A method for predicting one or more future incidents related to one or more previously detected objects, the method comprising:

• capturing a video stream using a camera;
• detecting an object in a current frame of the video stream and processing the current frame to generate a processed current frame that contains the detected object;
• determining a primary class of the detected object;
• generating a feature representation of the processed current frame by a convolutional encoder;
• clustering the feature representation in one or more primary clusters in a clustering space of the primary class, wherein the one or more primary clusters correspond to a sub-class of the primary class;
• generating an information vector of the feature representation, wherein the information vector includes a set of variables that include the primary class, corresponding sub-class, and one or more external factors associated with the feature representation;
• clustering each information vector of the video stream into one or more secondary clusters, wherein each variable in the information vector is a clustering dimension;
• reporting a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of the one or more secondary clusters, of an information vector of a previous frame;
• receiving at least one variable of the information vector as an input;
• and
• predicting at least one another variable of the information vector based on one or more previously formed secondary clusters.

A computer programmable product for predicting one or more future incidents related to one or more previously detected objects, the computer programmable product comprising a set of instructions, the set of instructions when executed by a processor causes the processor to:

• capture a video stream using a camera;
• detect an object in a current frame of the video stream and process a current frame to generate a processed current frame that contains the detected object;
• determine a primary class of the detected object;
• generate a feature representation of the processed current frame by a convolutional encoder;
• cluster the feature representation in one or more primary clusters in a clustering space of the primary class, wherein the one or more primary clusters correspond to a sub-class of the primary class;
• generate an information vector of the feature representation, wherein the information vector includes a set of variables that include the primary class, corresponding sub-class, and one or more external factors associated with the feature representation;
• cluster each information vector of the video stream into one or more secondary clusters, wherein each variable in the information vector is a clustering dimension;
• report a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of the one or more secondary clusters, of an information vector of a previous frame;
• receive at least one variable of the information vector as an input;
• and
• predict at least one another variable of the information vector based on one or more previous secondary clusters.