Pixel-level based micro-feature extraction

摘要

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

主权项

1. A computer-implemented method for extracting pixel-level micro-features from image data captured by a video camera, the method 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 plurality of micro-feature values; 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.