Anomalous pattern discovery
First Claim
1. A method for anomalous pattern discovery, the method comprising:
- tracking movement of an input object in an image field of input video data to determine a trajectory of the input object, wherein the input video data image field is partitioned into a plurality of different grids defining a matrix and the input object trajectory passes through a plurality of input object trajectory grids;
extracting global image features relative to the trajectory and local image features from each of the input object trajectory grids of the image field of the input video data;
generating an anomaly distribution detection confidence decision value for each of the input object trajectory grids as a function of fitting the extracted local image features to a learned feature model representing normal pattern distributions or abnormal pattern distributions that are defined for the grids;
generating a trajectory similarity value for the input object trajectory as a function of similarity of a parameterized representation of the extracted global image features to a learned trajectory model representing a normal trajectory or an abnormal trajectory;
finding a normalized sum of the generated anomaly distribution detection confidence decision values for the grids that include the tracked object trajectory; and
determining a fused anomaly decision value for the tracked object as a dynamic weighted combination of a product of the normalized sum and a local coefficient and a product of the trajectory similarity value and a global coefficient, wherein the local and global coefficients are dynamically determined from values that are inversely correlated to variances of the learned feature model and the learned trajectory model, and wherein the local and global coefficients sum to one.
2 Assignments
0 Petitions
Accused Products
Abstract
A trajectory of movement of an object is tracked in a video data image field that is partitioned into a plurality of different grids. Global image features from video data relative to the trajectory are extracted and compared to a learned trajectory model to generate a global anomaly detection confidence decision value as a function of fitting to the learned trajectory model. Local image features are also extracted for each of the image field grids that include object trajectory, which are compared to learned feature models for the grids to generate local anomaly detection confidence decisions for each grid as a function of fitting to the learned feature models for the grids. The global anomaly detection confidence decision value and the local anomaly detection confidence decision values for the grids are into a fused anomaly decision with respect to the tracked object.
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Citations
22 Claims
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1. A method for anomalous pattern discovery, the method comprising:
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tracking movement of an input object in an image field of input video data to determine a trajectory of the input object, wherein the input video data image field is partitioned into a plurality of different grids defining a matrix and the input object trajectory passes through a plurality of input object trajectory grids; extracting global image features relative to the trajectory and local image features from each of the input object trajectory grids of the image field of the input video data; generating an anomaly distribution detection confidence decision value for each of the input object trajectory grids as a function of fitting the extracted local image features to a learned feature model representing normal pattern distributions or abnormal pattern distributions that are defined for the grids; generating a trajectory similarity value for the input object trajectory as a function of similarity of a parameterized representation of the extracted global image features to a learned trajectory model representing a normal trajectory or an abnormal trajectory; finding a normalized sum of the generated anomaly distribution detection confidence decision values for the grids that include the tracked object trajectory; and determining a fused anomaly decision value for the tracked object as a dynamic weighted combination of a product of the normalized sum and a local coefficient and a product of the trajectory similarity value and a global coefficient, wherein the local and global coefficients are dynamically determined from values that are inversely correlated to variances of the learned feature model and the learned trajectory model, and wherein the local and global coefficients sum to one. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system, comprising:
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a processor, a computer readable memory and a computer readable storage medium; wherein the processor, when executing program instructions stored on the computer-readable storage medium via the computer readable memory; tracks movement of an input object in an image field of input video data to determine a trajectory of the input object, wherein the input video data image field is partitioned into a plurality of different grids defining a matrix and the input object trajectory passes through a plurality of input object trajectory grids; extracts global image features relative to the trajectory and local image features from each of the input object trajectory grids of the image field of the input video data; generates an anomaly distribution detection confidence decision values for each of the input object trajectory grids as a function of fitting the extracted local image features to a learned feature model representing normal pattern distributions or abnormal pattern distributions that are defined for the grids; generates a trajectory similarity value for the input object trajectory as a function of similarity of a parameterized representation of the extracted global image features to a learned trajectory model representing a normal trajectory or an abnormal trajectory; finds a normalized sum of the generated anomaly distribution detection confidence decision values for the grids that include the tracked object trajectory; and determines a fused anomaly decision value for the tracked object as a dynamic weighted combination of a product of the normalized sum and a local coefficient and a product of the trajectory similarity value and a global coefficient; and wherein the local and global coefficients are dynamically determined from values that are inversely correlated to variances of the learned feature model and the learned trajectory model, and wherein the local and global coefficients sum to one. - View Dependent Claims (12, 13, 14, 15, 16)
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17. An article of manufacture for anomalous pattern discovery, the article of manufacture comprising:
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a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising instructions that, when executed by a processor, cause the processor to; track movement of an input object in an image field of input video data to determine a trajectory of the input object, wherein the input video data image field is partitioned into a plurality of different grids defining a matrix and the input object trajectory passes through a plurality of input object trajectory grids; extract global image features relative to the trajectory and local image features from each of the input object trajectory grids of the image field of the input video data; generate an anomaly distribution detection confidence decision value for each of the input object trajectory grids as a function of fitting the extracted local image features to a learned feature model representing normal pattern distributions or abnormal pattern distributions that are defined for the grids; generate a trajectory similarity value for the input object trajectory as a function of similarity of a parameterized representation of the extracted global image features to a learned trajectory model representing a normal trajectory or an abnormal trajectory; find a normalized sum of the generated anomaly distribution detection confidence decision values for the grids that include the tracked object trajectory; and determine a fused anomaly decision value for the tracked object as a dynamic weighted combination of a product of the normalized sum and a local coefficient and a product of the trajectory similarity value and a global coefficient; and wherein the local and global coefficients are dynamically determined from values that are inversely correlated to variances of the learned feature model and the learned trajectory model, and wherein the local and global coefficients sum to one. - View Dependent Claims (18, 19, 20, 21, 22)
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Specification