ABNORMAL BEHAVIOR DETECTION SYSTEM AND METHOD USING AUTOMATIC CLASSIFICATION OF MULTIPLE FEATURES
First Claim
1. An abnormal behavior detection system using an automatic classification of multiple features, comprising:
- a feature extraction unit, used to extract features for one or more objects from monitoring data, and establishing feature sets of objects;
a behavior model establishing unit, receiving the feature set from the feature extraction unit;
then establishing and updating a behavioral model after a cluster analysis;
a behavior determining unit, receiving the feature set established by the feature extraction unit and the behavior model from the behavior model establishing unit, and further determining if there is an abnormal behavior; and
an output unit, used to output data of the behavior model and determination result of the object'"'"'s behavior.
2 Assignments
0 Petitions
Accused Products
Abstract
Described herein are a system and a method for abnormal behavior detection using automatic classification of multiple features. Features from various sources, including those extracted from camera input through digital image analysis, are used as input to machine learning algorithms. These algorithms group the features and produce models of normal and abnormal behaviors. Outlying behaviors, such as those identified by their lower frequency, are deemed abnormal. Human supervision may optionally be employed to ensure the accuracy of the models. Once created, these models can be used to automatically classify features as normal or abnormal. This invention is suitable for use in the automatic detection of abnormal traffic behavior such as running of red lights, driving in the wrong lane, or driving against traffic regulations.
-
Citations
15 Claims
-
1. An abnormal behavior detection system using an automatic classification of multiple features, comprising:
-
a feature extraction unit, used to extract features for one or more objects from monitoring data, and establishing feature sets of objects; a behavior model establishing unit, receiving the feature set from the feature extraction unit;
then establishing and updating a behavioral model after a cluster analysis;a behavior determining unit, receiving the feature set established by the feature extraction unit and the behavior model from the behavior model establishing unit, and further determining if there is an abnormal behavior; and an output unit, used to output data of the behavior model and determination result of the object'"'"'s behavior. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. An abnormal behavior detection method which performs automatic classification based on multiple features, comprising:
-
extracting a feature set of each object from the monitoring data; performing a feature cluster analysis based on similarity of all feature sets; establishing or updating a behavior model based on the cluster analysis, and thereby defining the behavior model as a normal behavior model or an abnormal behavior, wherein the behavior model is formed by collecting the plurality of feature sets from the objects in a period of time; comparing each object'"'"'s feature set with all behavior models, and determining whether the object fits the behavior model to identify the abnormal behavior, and outputting the behavior type. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
-
Specification