Patent ID: 9412024
Filing Date: 2016-08-09
CPC Classification: G06K,G06T

Claim Text:
1. A method for identifying an erroneous video from one or more videos, each of the one or more videos including a plurality of frames, the method comprising: generating first and second feature vectors for each of the plurality of frames of the one or more videos based on visual salience and scene statistics extraction approaches, respectively, wherein each of the first and second feature vectors includes a plurality of feature data points; performing anomaly detection on the first and second feature vectors based on an isolation forest approach to calculate first and second average path lengths, respectively, corresponding to each of the plurality of frames; performing anomaly detection on the first and second feature vectors based on a nearest neighbour approach to calculate first and second anomaly measures, respectively, corresponding to each of the plurality of frames; segmenting each of the one or more videos into one or more video shots, wherein each of the one or more video shots includes a set of frames; determining first and second sets of average path lengths and first and second sets of anomaly measures corresponding to a first set of frames associated with a first video shot, wherein the first set of average path lengths includes a plurality of first average path lengths calculated corresponding to the first set of frames based on the isolation forest approach and the second set of average path lengths includes a plurality of second average path lengths calculated corresponding to the first set of frames based on the isolation forest approach, wherein the first set of anomaly measures includes a plurality of first anomaly measures calculated corresponding to the first set of frames based on the nearest neighbour approach and the second set of anomaly measures includes a plurality of second anomaly measures calculated corresponding to the first set of frames based on the nearest neighbour approach; determining first and second maximum average path lengths of the first and second sets of average path lengths, respectively; determining first and second maximum anomaly measures of the first and second sets of anomaly measures, respectively; dividing each of the first and second sets of average path lengths with the first and second maximum average path lengths, respectively, to obtain first and second sets of normalized average path lengths; dividing each of the first and second sets of anomaly measures with the first and second maximum anomaly measures, respectively, to obtain first and second sets of normalized anomaly measures; determining a plurality of first and second sets of normalized average path lengths and a plurality of first and second sets of normalized anomaly measures corresponding to one or more sets of frames corresponding to the one or more video shots; and identifying the erroneous video by providing at least one of the plurality of first and second sets of normalized average path lengths and at least one of the plurality of first and second sets of anomaly measures to a regression model including a plurality of model coefficients determined previously using a set of training videos.