Patent ID: 8660368
Filing Date: 2014-02-25
Classification: G06K

Abstract:
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.