Patent application number | Description | Published |
20110043625 | SCENE PRESET IDENTIFICATION USING QUADTREE DECOMPOSITION ANALYSIS - Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene. | 02-24-2011 |
20110044492 | ADAPTIVE VOTING EXPERTS FOR INCREMENTAL SEGMENTATION OF SEQUENCES WITH PREDICTION IN A VIDEO SURVEILLANCE SYSTEM - A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies. | 02-24-2011 |
20110044498 | VISUALIZING AND UPDATING LEARNED TRAJECTORIES IN VIDEO SURVEILLANCE SYSTEMS - Techniques are disclosed for visually conveying a trajectory map. The trajectory map provides users with a visualization of data observed by a machine-learning engine of a behavior recognition system. Further, the visualization may provide an interface used to guide system behavior. For example, the interface may be used to specify that the behavior recognition system should alert (or not alert) when a particular trajectory is observed to occur. | 02-24-2011 |
20110044536 | PIXEL-LEVEL BASED MICRO-FEATURE EXTRACTION - Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors. | 02-24-2011 |
20110050897 | VISUALIZING AND UPDATING CLASSIFICATIONS IN A VIDEO SURVEILLANCE SYSTEM - Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification. | 03-03-2011 |
20110052000 | DETECTING ANOMALOUS TRAJECTORIES IN A VIDEO SURVEILLANCE SYSTEM - Techniques are disclosed for determining anomalous trajectories of objects tracked over a sequence of video frames. In one embodiment, a symbol trajectory may be derived from observing an object moving through a scene. The symbol trajectory represents semantic concepts extracted from the trajectory of the object. Whether the symbol trajectory is anomalous may be determined, based on previously observed symbol trajectories. A user may be alerted upon determining that the symbol trajectory is anomalous. | 03-03-2011 |
20110052067 | CLUSTERING NODES IN A SELF-ORGANIZING MAP USING AN ADAPTIVE RESONANCE THEORY NETWORK - Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects. | 03-03-2011 |
20110052068 | IDENTIFYING ANOMALOUS OBJECT TYPES DURING CLASSIFICATION - Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types. | 03-03-2011 |
20110064267 | CLASSIFIER ANOMALIES FOR OBSERVED BEHAVIORS IN A VIDEO SURVEILLANCE SYSTEM - Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior. | 03-17-2011 |
20110064268 | VIDEO SURVEILLANCE SYSTEM CONFIGURED TO ANALYZE COMPLEX BEHAVIORS USING ALTERNATING LAYERS OF CLUSTERING AND SEQUENCING - Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events. | 03-17-2011 |
20120224746 | CLASSIFIER ANOMALIES FOR OBSERVED BEHAVIORS IN A VIDEO SURVEILLANCE SYSTEM - Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior. | 09-06-2012 |
20130022242 | IDENTIFYING ANOMALOUS OBJECT TYPES DURING CLASSIFICATION - Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types. | 01-24-2013 |
20130242093 | ALERT DIRECTIVES AND FOCUSED ALERT DIRECTIVES IN A BEHAVIORAL RECOGNITION SYSTEM - Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures. | 09-19-2013 |
20130243252 | LOITERING DETECTION IN A VIDEO SURVEILLANCE SYSTEM - A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to learn patterns of behavior consistent with a person loitering and generate alerts for same. Upon receiving information of a foreground object remaining in a scene over a threshold period of time, a loitering detection module evaluates the whether the object trajectory corresponds to a random walk. Upon determining that the trajectory does correspond, the loitering detection module generates a loitering alert. | 09-19-2013 |
20140002647 | ANOMALOUS STATIONARY OBJECT DETECTION AND REPORTING | 01-02-2014 |