Pixel-level based micro-feature extraction

A non-transitory computer-readable storage medium containing a program which, when executed by a processor, performs an operation for extracting pixel-level micro-features from image data captured by a video camera, the operation comprising:

• receiving the image data;
• identifying a set of pixels in the image data associated with a foreground patch that depicts a foreground object;
• evaluating appearance values of the pixels included in the set of pixels to compute a plurality of micro-feature values representing the foreground object, each based on at least one pixel-level characteristic of the foreground patch, wherein the micro-feature values are computed independent of training data that defines a plurality of object types;
• generating a micro-feature vector that includes the micro-feature value;
• and
• classifying the foreground object as depicting an object type as based on the micro-feature vector, wherein the object type is determined by mapping the micro-feature vector to a cluster in a self-organizing map (SOM) adaptive resonance theory (ART) network generated from a plurality of micro-feature vectors;
• and
• updating one or more cluster properties associated with the cluster based on the plurality of micro-feature values in the generated micro-feature vector.

A system, comprising:

• a video input source configured to provide image data;
• a processor;
• and
• a memory containing a program, which, when executed on the processor is configured to perform an operation for extracting pixel-level micro-features from the image data captured by the video input source, the operation comprising:
• receiving the image data;
• identifying a set of pixels in the image data associated with a foreground patch that depicts a foreground object;
• evaluating appearance values of the pixels included in the set of pixels to compute a plurality of micro-feature values representing the foreground object, each based on at least one pixel-level characteristic of the foreground patch, wherein the micro-feature values are computed independent of training data that defines a plurality of object types;
• generating a micro-feature vector that includes the micro-feature value;
• classifying the foreground object as depicting an object type as based on the micro-feature vector, wherein the object type is determined by mapping the micro-feature vector to a cluster in a self-organizing map (SOM) adaptive resonance theory (ART) network generated from a plurality of micro-feature vectors;
• and
• updating one or more cluster properties associated with the cluster based on the plurality of micro-feature values in the generated micro-feature vector.