VIDEO ANALYSIS METHODS AND APPARATUS

• US 20170178343A1
• Filed: 01/19/2016
• Published: 06/22/2017
• Est. Priority Date: 12/16/2015
• Status: Active Grant

• Current Assignee: Oneberry Technologies Pte., Ltd.
• Original Assignee: Vi Dimensions Pte Ltd.
• Inventors: Maranatha, Yosua Michael, Tay Meng Keat, Christopher, Looi, Raymond, Sriram, Ashish
• Granted Patent: US 10,217,226 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06V 10/776 Validation; Performance evaluation
• CPC Class Codes: G06V 10/809 of classification results, e.g. where the classifiers operate on the same input data
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 Event detection
• CPC Class Codes: G06V 20/46 Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69

Abstract

Video analysis methods are described in which abnormalities are detected by comparing features extracted from a video sequence or motion patterns determined from the video sequence with a statistical model. The statistical model may be updated during the video analysis.

Claims

1-46. -46. (canceled)

47. A method of identifying abnormal events in a video sequence, the method comprising:

extracting features from the video sequence;

determining an abnormality measure for each feature by comparing the extracted features with a statistical model; and

identifying an abnormal event using the abnormality measure.

48. A method according to claim 47, further comprising discretizing the extracted features.

49. A method according to claim 48, wherein the statistical model comprises a histogram indicating a frequency distribution of discretized features extracted from the video sequence and the abnormality measure is determined from the frequency distribution.

50. A method according to claim 49, further comprising updating the frequency distribution with the discretized extracted feature.

51. A method according to claim 50, further comprising pruning the updated frequency distribution if the updated frequency distribution exceeds a threshold number of entries.

52. A method according to claim 49, further comprising determining for a portion of the video sequence, a set of discretized features present in the portion of the video sequence and a frequency of occurrence in the portion of the video sequence of each discretized feature of the set of discretized features.

53. A method according to claim 52, further comprising determining an abnormality measure for the portion of the video sequence as a function of the abnormality measures for each of the discretized features and the frequency of occurrence of the discretized features.

54. A method according to claim 47, wherein identifying an abnormal event using the abnormality measure comprises comparing the abnormality measure with a threshold and identifying an abnormal event when the abnormality measure is greater than the threshold.

55. A method according to claim 47, further comprising displaying to a user an indication of the location of the abnormal even on a frame of the video sequence.

56. A method according to claim 49, further comprising receiving a user indication of a set of discretized features and modifying the frequency in the frequency distribution of the indicated set of discretized features.

57. A method according to claim 47, wherein the features comprise optical flow.

58. A method according to claim 47, further comprising detecting an event according to a pre-defined rule and wherein identifying an abnormal event comprises using the abnormality measure and the result of the pre-defined rule.

59. A non-transitory computer readable carrier medium carrying processor executable instructions which when executed on a processor cause the processor to carry out a method according to claim 47.

60. An apparatus for identifying abnormal events in a video sequence, the apparatus comprising:

a computer processor and a data storage device, the data storage device having a feature extractor module and an abnormality detector module comprising non-transitory instructions operative by the processor to;

extract features from the video sequence;

determine an abnormality measure for each feature by comparing the extracted features with a statistical model; and

identify an abnormal event using the abnormality measure.

61. An apparatus according to claim 60, wherein the features comprise optical flow.

62. A method of identifying abnormal events in a video sequence, the method comprising:

extracting features from the video sequence;

determining motion patterns from the extracted features;

determining an abnormality measure for each motion pattern by comparing the motion patterns with a statistical model; and

identifying an abnormal event using the abnormality measure.

63. A method according to claim 62, wherein the motion patterns comprise tracks indicating motion between frames of the video sequence.

64. A method according to claim 62, further comprising quantizing the extracted features.

65. A method according to claim 62, further comprising updating the statistical model using the motion patterns.

66. A method according to claim 62, wherein identifying an abnormal event using the abnormality measure comprises comparing the abnormality measure with a threshold and identifying an abnormal event when the abnormality measure is greater than the threshold.

67. A method according to claim 62, wherein the statistical model comprises a set of clustered motion patterns.

68. A method according to claim 67, wherein determining an abnormality measure for a motion pattern comprises identifying a set of clustered motion patterns of the statistical model closest to the motion pattern.

69. A method according to claim 68, wherein the abnormality measure is a distance measure between the set of clustered motion patterns closest to the motion pattern and the motion pattern.

70. An apparatus for identifying abnormal events in a video sequence, the apparatus comprising:

a computer processor and a data storage device, the data storage device having a feature extraction module;

a motion pattern generation module and a classifier module comprising non-transitory instructions operative by the processor to;

extract features from the video sequence;

determine motion patterns from the extracted features;

determine an abnormality measure for each motion pattern by comparing the motion patterns with a statistical model; and

identify an abnormal event using the abnormality measure.