Identifying anomalous object types during classification
First Claim
1. A computer-implemented method for identifying anomaly object types during classification of image data captured by a video camera, the method comprising:
- receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data;
classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector;
computing a probability density function for the object type clusters;
computing a probability density value for the micro-feature vector;
evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value; and
identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold.
6 Assignments
0 Petitions
Accused Products
Abstract
Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters 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. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
-
Citations
20 Claims
-
1. A computer-implemented method for identifying anomaly object types during classification of image data captured by a video camera, the method comprising:
-
receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector; computing a probability density function for the object type clusters; computing a probability density value for the micro-feature vector; evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value; and identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for identifying anomaly object types during classification of image data captured by a video camera, the operation comprising:
-
receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector; computing a probability density function for the object type clusters; computing a probability density value for the micro-feature vector; evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value; and identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold. - View Dependent Claims (10, 11, 12, 13)
-
-
14. 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 that identifies anomaly object types during classification of image data captured by a video camera, the operation comprising; receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector; computing a probability density function for the object type clusters; computing a probability density value for the micro-feature vector; evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value, and identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold. - View Dependent Claims (15, 16, 17, 18, 19, 20)
-
Specification