Source: https://insight.rpxcorp.com/pat/US20110052067A1
Timestamp: 2019-10-18 01:22:34
Document Index: 629328376

Matched Legal Cases: ['ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART1', 'ART2', 'ART 325', 'ART3', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325', 'ART 325']

Patent US 20110052067A1
1. A computer-implemented method for discovering object type clusters for image data captured by a video camera, the method comprising:
receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object;
processing the micro-feature vector by a self-organizing map adaptive resonance theory (SOM-ART) network to discover the object type clusters for the image data;
classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters when the micro-feature vector matches the first object type cluster; and
indicating that no match is found when the micro-feature vector does not correspond to any of the object type clusters for the image data.
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.
US 20110119477A1
Byun Sung-Jae, Lee Young Min, Hwang Gyoo-Cheol, Lee Yun-Tae
US 20130282630A1
Tagasauris Inc.
US 8,984,237 B2
US 9,489,636 B2
receiving additional micro-feature vectors; and
updating the SOM-ART network when a predetermined number of the additional micro-feature vectors is received to produce an updated SOM-ART network.
determining that a micro-feature value is invalid; and
excluding the invalid micro-feature value from the updating of the SOM-ART network.
4. The computer-implemented method of claim 1, further comprising estimating a topology for the SOM-ART network based on the number of input micro-feature vectors.
5. The computer-implemented method of claim 1, further comprising merging the first object type cluster with a second object type cluster of the object type clusters for the image data when the first object type cluster overlaps the second object type cluster by a specified amount.
removing the first object type cluster when none of the additional micro-feature vectors match the first object type cluster.
7. The computer-implemented method of claim 1, further comprising adding a new object type cluster when the micro-feature vector does not correspond to any of the object type clusters for the image data.
processing the additional micro-feature vectors to produce a mature SOM-ART network before classifying the foreground patch.
9. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for discovering object type clusters for image data captured by a video camera, the operation comprising:
12. The computer-readable storage medium of claim 9, wherein the operation further comprises estimating a topology for the SOM-ART network based on the number of input micro-feature vectors.
a video input source configured to provide image data;
a memory containing a program, which, when executed on the processor is configured to perform an operation discovering object type clusters for the image data captured by the video input source, the operation comprising;
15. The system of claim 14, wherein the operation further comprises:
17. The system of claim 14, wherein the operation further comprises comprises estimating a topology for the SOM-ART network based on the number of input micro-feature vectors.
18. The system of claim 14, wherein the operation further comprises:
19. The system of claim 14, wherein the operation further comprises:
20. The system of claim 14, wherein the operation further comprises adding a new object type cluster when the micro-feature vector does not correspond to any of the object type clusters for the image data.
Embodiments of the invention provide techniques for discovering object type clusters using pixel-level micro-features extracted from image data. More specifically, embodiments of the invention relate to techniques for producing and updating a self-organizing map and adaptive resonance theory (SOM-ART) network that is used to classify objects depicted in the image data based on the pixel-level micro-features.
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera. Further, such systems may be configured to determine the type of object that the “blob” depicts.
Embodiments of the invention relate to techniques for a micro-feature classifier component to discover object type clusters using pixel-level micro-features extracted from image data. The object type clusters are discovered and updated by a self-organizing map and adaptive resonance theory (SOM-ART) network. The SOM-ART network is produced and updated based on the pixel-level micro-features independent of any object definition data, i.e., without training. Therefore, unsupervised learning, cluster discovery, and object type classification may be performed in parallel.
FIG. 3A illustrates an example of a micro-feature classifier component of the video analysis system shown in FIG. 2, according to one embodiment of the invention.
FIG. 3B illustrates a method for discovering object type clusters using pixel-level based micro-features, according to one embodiment of the invention.
FIG. 3C illustrates a conceptual diagram of the SOM after the N dimension micro-feature vectors have been processed, according to one embodiment of the invention.
FIG. 4A illustrates a method for performing unsupervised learning using the SOM-ART network, according to one embodiment of the invention.
FIG. 4B illustrates a method for performing object type classification using the SOM-ART network, according to one embodiment of the invention.
Embodiments of the invention discover object type clusters based on pixel-level micro-features that are extracted from one or more images. The extracted micro-features are heuristic features of foreground patches depicting objects in frames of video. The extracted micro-features may be represented as a micro-feature vector input to the micro-feature classifier. The micro-feature classifier may learn a set of distinct object types, over time, through observing different micro-feature vectors. In one embodiment, a SOM-ART network is included within the micro-feature classifier to process the pixel-level micro-features to adaptively learn and organize the micro-features into object type clusters. The training of the SOM-ART network is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to perform unsupervised learning and object classification in parallel without being constrained by specific object definitions.
The micro-feature extractor and micro-feature classifier may be included within a behavior-recognition system which may be configured to identify, learn, and recognize patterns of behavior by observing and evaluating events depicted by a sequence of video frames. In a particular embodiment, the behavior-recognition system may include both a computer vision engine and a machine learning engine. The computer vision engine may be configured to receive and evaluate a stream of video frames. Each frame may include data representing the color, grayscale, and/or intensity values for each pixel in the frame. A frame of video may be characterized using multiple color channels (e.g., a radiance value between 0-255 and a set of red, green, and blue (RGB) color channels values, each between 0-255). Further, the computer vision engine may generate a background image by observing the scene over a number of video frames. For example, consider a video camera trained on a stretch of a highway. In such a case, the background would include the roadway surface, the medians, any guard rails or other safety devices, and traffic control devices, etc., that are visible to the camera. Vehicles traveling on the roadway (and any other person or thing engaging in some activity) that are visible to the camera would represent scene foreground objects.
The computer vision engine may compare the pixel values for a given frame with the background image and identify objects as they appear and move about the scene. Typically, when a group of pixels in the scene (referred to as a “blob” or “patch”) is observed with appearance values that differ substantially from the background image, that region is identified as a foreground patch that likely depicts a foreground object. As described in greater detail below, pixel-level characteristics of the foreground patch are computed and used to extract pixel-level micro-features that are represented as a micro-feature vector. The micro-feature vector corresponding to the foreground patch may be evaluated to allow the system to distinguish among different types of foreground objects (e.g., a vehicle or a person) on the basis of the micro features. Further, the computer vision engine may identify features (e.g., height/width in pixels, color values, shape, area, pixel distributions, and the like) used to track the object from frame-to-frame. Further still, the computer vision engine may derive a variety of information while tracking the object from frame-to-frame, e.g., position, current (and projected) trajectory, direction, orientation, velocity, rigidity, acceleration, size, and the like. In one embodiment, the computer vision outputs this information and/or the micro-feature vector as a stream describing a collection of kinematic information related to each foreground patch in the video frames.
Data output from the computer vision engine may be supplied to the machine learning engine. In one embodiment, the machine learning engine may evaluate the context events to generate “primitive events” describing object behavior. Each primitive event may provide some semantic meaning to a group of one or more context events. For example, assume a camera records a car entering a scene, and that the car turns and parks in a parking spot. In such a case, the computer vision engine could initially recognize the car as a foreground object; classify it as being a vehicle (or least classify it as being an instance of an arbitrary object-type based on SOM-ART clusters of micro-feature vectors), and output kinematic data describing the position, movement, speed, etc., of the car in the context event stream. In turn, a primitive event detector could generate a stream of primitive events from the context event stream such as “vehicle appears,” vehicle turns,” “vehicle slowing,” and “vehicle stops” (once the kinematic information about the car indicated a speed of 0). As events occur, and re-occur, the machine learning engine may create, encode, store, retrieve, and reinforce patterns representing the events observed to have occurred, e.g., long-term memories representing a higher-level abstraction of a car parking in the scene—generated from the primitive events underlying the higher-level abstraction. Further still, patterns representing an event of interest may result in alerts passed to users of the behavioral recognition system.
In one embodiment, the machine learning engine 140 receives the video frames and the data generated by the computer vision engine 135. The machine learning engine 140 may be configured to analyze the received data, classify objects, build semantic representations of events depicted in the video frames, detect patterns, and, ultimately, to learn from these observed patterns to identify normal and/or abnormal events. Additionally, data describing whether a normal/abnormal behavior/event has been determined and/or what such behavior/event is may be provided to output devices 118 to issue alerts, for example, an alert message presented on a GUI interface screen. In general, the computer vision engine 135 and the machine learning engine 140 both process video data in real-time. However, time scales for processing information by the computer vision engine 135 and the machine learning engine 140 may differ. For example, in one embodiment, the computer vision engine 135 processes the received video data frame-by-frame, while the machine learning engine 140 processes data every N-frames, where N is greater than or equal to 1. In other words, while the computer vision engine 135 analyzes each frame in real-time to derive a set of information about what is occurring within a given frame, the machine learning engine 140 is not constrained by the real-time frame rate of the video input.
The context processor component 220 may receive the output from other stages of the pipeline (i.e., the tracked objects and the background and foreground models). Using this information, the context processor 220 may be configured to generate a stream of micro-feature vectors corresponding to foreground patches tracked (by tracker component 210). For example, the context processor component 220 may evaluate a foreground patch from frame-to-frame and output micro-feature vectors including values representing the foreground patch's hue entropy, magnitude-saturation ratio, orientation angle, pixel area, aspect ratio, groupiness (based on the pixel-level spatial distribution), legged-ness (based on a number of potential legs), verticality (based on per-pixel gradients), motion vector orientation, rigidity/animateness, periodicity of motion, etc. Examples of Valid micro-feature values may range in value from 0 to 1 (inclusive) and −1 may be used to represent an invalid micro-feature value that should not be used for classification. Additionally, the context processor component 220 may output a stream of context events describing that foreground patch's height, width (in pixels), position (as a 2D coordinate in the scene), acceleration, velocity, orientation angle, etc. The computer vision engine 135 may take the outputs of the components 205, 210, and 220 describing the motions and actions of the tracked foreground patches in the scene and supply this information to the machine learning engine 140.
In some systems, the computer vision engine is configured to classify each tracked object as being one of a known category of objects using training data that defines a plurality of object types. For example, an estimator/identifier component may be included within the computer vision engine to classify a tracked object as being a “person,” a “vehicle,” an “unknown,” or an “other.” In this context, the classification of “other” represents an affirmative assertion that the object is neither a “person” nor a “vehicle.” Additionally, the estimator/identifier component may identify characteristics of the tracked object, e.g., for a person, a prediction of gender, an estimation of a pose (e.g., standing or sitting) or an indication of whether the person is carrying an object. Such an estimator/identifier component is provided with training data that specifies a plurality of objects and is used to perform the classification.
In contrast, systems that do not include an estimator/identifier component, such the computer vision engine 135 shown in FIG. 2, the classification of objects is performed by the micro-feature classifier 221 in the machine learning engine 140 using the micro-feature vectors that are produced by the computer vision engine 135 independent of any training data. By processing the foreground patches independent of training data, extraction and classification may begin earlier and can adapt to recognize a variety of different object types dependent on the specific image data. In particular, since the range of object types is not defined by training data, the range is also not restricted. When micro-features are used to classify, objects with similar micro-feature vectors are automatically grouped together in object type clusters. In some embodiments, the micro-feature classifier 221 may use a combination of a self-organizing map (SOM) adaptive resonance theory (ART) network to assign micro-feature vectors to clusters. In such a case, each cluster represents a distinct object type, without the distinct types having to be defined in advance. Additionally, in some embodiments the behavior recognition system 100 may be configured to present the foreground objects in a particular object type cluster to a user in order to allow the user to specify an object type label for the cluster.
FIG. 3A illustrates an example of the micro-feature classifier component 221 of the video analysis system shown in FIG. 2, according to one embodiment of the invention. The micro-feature classifier component 221 receives a micro-feature vector 300 that is produced by the context processor component 220 and outputs an identified object type 345. The context processor component 220 processes the image data and produces pixel-level characteristic(s) that are used to generate the micro-feature vectors 300. Micro-feature values are computed by the context processor component 220 and output as elements of the micro-feature vectors 300. Examples of micro-feature values include values representing the foreground patch's hue entropy, magnitude-saturation ratio, orientation angle, pixel area, aspect ratio, groupiness (based on the pixel-level spatial distribution), legged-ness, verticality (based on per-pixel gradients), animateness, periodicity of motion, etc. Valid micro-feature values may range in value from 0 to 1 (inclusive) and −1 may be used to represent an invalid micro-feature value that should not be used for classification. The micro-feature values may be represented in a floating point format.
The micro-feature classifier 221 includes a learning component 310, a SOM-ART network component 340, and a classification component 320. The SOM-ART network component 340 includes a SOM 315 and an ART 325. The SOM-ART network component 340 provides a specialized neural network configured to create object type clusters from a group of inputs, e.g., micro-feature vectors. Each element (micro-feature value) of the micro-feature vector is a dimension of an input to the SOM 315. The SOM 315 receives the N dimension micro-feature vector and produces a reduced dimension geometric representation of M dimensions, where M is less than or equal to N. Reducing the number of dimensions also reduces the time duration for self-training since the amount of input data needed to represent the distribution of the clusters is reduced. For example, when N is 6, the number of different input values is 8<sup>6</sup>=262,144 (assuming an 8 bit micro-feature value). Reducing the number of dimensions from 6 to 5, reduces the number of different input values from 262,144 to 32,768.
The ART 325 is updated by the learning component 310 to manage the object type clusters, creating new clusters, removing clusters, and merging clusters. The ART 325 is used by the classification component 320 to classify each foreground object depicted by a micro-feature vector as one of the learned object types or as an unrecognized (unknown) object type. The learning component 310 is not limited to specific pre-defined object types since the learning component 310 is configured to perform unsupervised learning to automatically find what and how many object types may exist in the image data. This unsupervised learning and object type cluster discovery are adaptive since the knowledge about existing classes of objects is dynamically updated as new object types appear and may occur in parallel with the classification functions performed by the classification component 320. Therefore, the micro-feature classifier 221 is suitable for real-time video surveillance applications. When the image data represents a new scene or at startup, the learning component 310 estimates the SOM 315 topology based on the number of the input micro-feature vectors. A principal component analysis (PCA) technique may be used to initialize the SOM 315. The learning component 310 may process a first batch of micro-feature vectors to produce a mature SOM 315 before the classification component 325 processes any of the micro-feature vectors in the first batch. The size of the first and following batches may be adjusted to fit the incoming micro-feature data stream rate.
The learning component 310 may update the SOM-ART component 340 incrementally, i.e. as each micro-feature vector is received. Alternatively, the learning component 310 may collect a batch (predetermined number) of micro-feature vectors and update the SOM-ART component 340 periodically. The learning component 310 organizes the micro-feature vector elements in the SOM 315 neurons and then updates object type clusters in the ART neurons incrementally or periodically. The classification component 320 processes each micro-feature vectors that is received by comparing the vectors' micro-feature values with the existing object type clusters in the SOM-ART component 221. The object depicted by a micro-feature vector is classified by the classification component 320 as one of the learned object types or as an unrecognized (unknown) object type based on the distance measure between the received micro-feature vector and the object type clusters in the SOM 315.
FIG. 3B illustrates a method for discovering object type clusters using pixel-level based micro-features, according to one embodiment of the invention. The method begins at step 350 when a micro-feature vector is received by the micro-feature classifier 221. At step 355 the SOM-ART network is estimated by the SOM-ART network component 340 to produce an initial SOM 315. Once the SOM-ART network is estimated, the micro-feature vectors that were used to perform the estimation may be classified. At step 360 the micro-feature classifier 221 performs unsupervised learning, cluster discovery, and object type classification. The learning component 310 updates the SOM 315 and ART 325 periodically or incrementally.
At step 365 the machine learning engine 140 determines if a reset condition has occurred. A reset condition occurs when the scene being viewed changes dramatically or a new scene is viewed. Steps 360 and 365 are repeated until a reset condition does occur, and then at step 370 the machine learning engine 140 determines if information stored for a previously viewed scene may be restored to accelerate the learning process compared with initiating the learning process for a new scene. If, at step 370 the information stored for the previously viewed scene may be restored, then at step 375 the current SOM 315 and ART 325 information is stored and the information for the previously viewed scene is loaded into SOM 315 and ART 325 and the micro-feature classifier 221 proceeds to step 372.
When information stored for a previously viewed scene is not available to be restored at step 370, the machine learning engine 140 proceeds directly to step 372. At step 372 the micro-feature classifier 221 determines if a new micro-feature vector is received. When a new micro-feature vector is received in step 372, the micro-feature classifier 221 begins processing image data for the new or restored scene by returning to step 355. Otherwise, the micro-feature classifier 221 waits for a new micro-feature vector.
FIG. 3C illustrates a conceptual diagram of the SOM 315 after the N dimension micro-feature vectors have been processed, according to one embodiment of the invention. The SOM 315 converts the N dimensional micro-feature vectors that represent complex statistical relationships of the image data into simpler geometric relationships that may be represented in fewer (M) dimensions. The topological and metric relationships of the micro-feature vectors are preserved by the SOM 315 while the number of dimensions is reduced. The SOM 315 may include a two dimensional regular grid of nodes. Each node corresponds to a model that is computed based on the micro-feature vectors that are received. Neighboring models are similar and models that are a greater distance apart are dissimilar. In other words, the SOM 315 is a clustering diagram. The SOM 315 is computed using techniques known to those skilled in the art. In particular, the SOM 315 may be computed using a nonparametric, recursive regression process.
A node 405 may correspond to a micro-feature that is predominately shiny in terms of a pixel-level characteristic. Nodes near to the node 405 correspond to micro-features that are also shiny in addition to having other characteristics. A node 410 may correspond to a micro-feature that has a strong groupiness characteristic, perhaps indicative of a bicycle object type. A node 415 may correspond to a micro-feature that has a strong verticality characteristic, perhaps indicative of a person.
Each object type cluster itself may be characterized by a mean micro-feature vector and variances from a prototype input representing that cluster. The prototype is generated first, as a copy of the input vector used to create a new object type cluster. Subsequently, the prototype may be updated as new inputs are mapped to that object type cluster. Additionally, an object type cluster may be characterized by how many input vectors have been used to update that object type cluster—after it is initially created. Typically, the more input vectors that map to a given object type cluster, the more significant that object type cluster.
For example, the ART 325 may receive a micro-feature vector 300 as input and either update an existing cluster or create a new object type cluster, as determined using a choice test and a vigilance test for the ART 325. The choice and vigilance tests are used to evaluate the micro-feature vector 300 passed to the ART 325. The choice test provides a ranking of the existing object type clusters, relative to the micro-feature vector input data in the ART 325. Once ranked, the vigilance test evaluates the existing object type clusters to determine whether to map the foreground patch to a given object type cluster. If no object type cluster is found to update using the data supplied to the ART 325, evaluated sequentially using the ranked object type clusters, then a new object type cluster is created. That is, once a pattern is found (i.e., the input “matches” an existing cluster according to the choice and vigilance tests), the prototype for that object type cluster is updated based on the values of the input micro-feature vector 300. Otherwise, if the micro-feature vector 300 does not match any available object type cluster (using the vigilance test), a new object type cluster is created by storing a new pattern similar to the micro-feature vector 300. Subsequent micro-feature vectors that most closely resemble the new object type cluster (relative to the others) are then used to update that object type cluster.
As is known, the vigilance parameter has considerable influence on an ART 325: higher vigilance produces many, fine-grained clusters, where a lower vigilance results in more-general clusters. Further, the inputs may be a binary values (generally referred to as an ART1 network), or may be continuous values (generally referred to as an ART2 network). Other variations of the ART 325 include ART3, ARTMAP, and FUZZY ART networks.
In one embodiment, the ART 325 may be configured to provide dynamic cluster sizes. For example, each cluster may be given an initial shape and size, such as a radius of 5-10. Each new input to the ART 325 is then used to update the size of a cluster for each dimension of input data (or create a new cluster). Consider, e.g., a ART 325 which receives a micro-feature vector 300 that includes (x, y, h, w) representing a position (x, y) of an object in a frame of video (e.g., a foreground object classified as a person) having a height (h) and width (w) (in pixels). This example results in clusters in a 4 dimensional space—a hyper-ellipsoid. In such a case, clusters may be defined using a mean and variance for a cluster in each of the four dimensions. As new input micro-feature vectors 300 are mapped the cluster, the mean and variance for each dimension may be updated, changing the position, shape and size of the cluster. Alternatively, the clusters may be defined using a mean and a covariance. Doing so results in a more accurate boundary for each cluster. However, using a covariance approach increases the computational complexity. Thus, the actual approach may be tailored to suit the needs of a particular case. Further, by projecting the cluster into a two-dimensional plane (x, y), the resulting shape and position of the cluster correspond to a region in the scene where the events being categorized by the ART 325 have been observed. Thus, for an ART 325 that categorizes the position (and pixel width and height) of a person, each cluster identifies an area in the scene where people have, e.g., appeared, disappeared, or simply been observed to be present.
Additionally, in one embodiment, the ART 325 may also be configured to provide for cluster decay. For example, the ART 325 may be configured to require that a cluster be periodically reinforced in order to remain in the ART 325. In such a case, if a new cluster is created, but no new micro-feature vectors have been mapped to that cluster for a specified period, then the learning component 310 may remove the cluster from the ART 325. Doing so improves the efficiency of the ART 325 by not retaining clusters of little (or no) significance. Further, doing so helps to account for the reality that the events observed in a scene are expected to change over time. That is, while a cluster may be significant at one time (e.g., because people are repeatedly observed to appear at a first location), the patterns of behavior being observed may change (e.g., people being observed to appear at a second location).
As clusters emerge in the ART 325, the ART 325 may be evaluated to determine whether an unusual event has occurred, based on a set of alert rules. For example, consider the scenario when the ART 325 is configured to categorize objects of the “person” class, based on the position at which a person appears (or disappears) from the scene. In such a case, each cluster describes a prototypical position of where a person may appear—and a mean and variance from that prototypical position (e.g., to 2.5 standard deviations in the X and Y directions). In this scenario, an alert rule may specify that whenever the ART 325 generates a new cluster based on a set of parsed input values, an alert should be generated. Further, as the decision of the ART 325 to create a new cluster is dependent on whether a given input sufficiently resembles one of the current clusters, in one embodiment, the ART 325 is allowed to “cool” for specified period of time prior to any alerts being generated. That is, until the computer vision engine 135 has observed a sufficient number of persons, new clusters may be created with a relatively high frequency. Conversely, after prolonged observation, the relative frequency of new clusters should decline—making the event of a new cluster more unusual. Of course, one of skill in the art that the alert rules may be based on a broad variety of triggering conditions based on the state of the ART 325—and that the actual alert rules may be tailored for the ART 325 and by the needs of a particular case.
As stated, clusters of the ART 325 may dynamically expand and contract by learning—as the mean and variance from the prototypical cluster value changes based on inputs to the ART 325. Further, multiple clusters may collapse to a single cluster when they overlap by a specified amount (e.g., the two clusters share greater than a specified percentage of their area). In such a case, the mean and variance of each cluster contributes to the mean and variance of the merged cluster. Additionally, the statistical significance of each cluster participating in the merger may contribute to a significance determined for the merged cluster. Also as stated, the micro-feature classifier 221 may track how many inputs to the ART 325 are mapped to a particular cluster in the ART 325. Typically, the more inputs that map to a cluster, the greater the relative significance of that cluster. In one embodiment, the relative importance of a given cluster may contribute to the determination of whether to generate an alert (according to the alert rules) when a new cluster is created (or otherwise). For example, if the ART 325 has many clusters, all of relatively equal significance, then the creation of a new cluster may be a relatively minor event. Conversely, if the ART 325 has a small number of clusters of disproportionate significance (relative to other clusters in the ART 325) then the creation of a new cluster may be a much more unusual event.
FIG. 4A illustrates a method for performing unsupervised learning using the SOM-ART network component 340, according to one embodiment of the invention. At step 400 a micro-feature vector for a foreground patch is received by the micro-feature classifier 221. At step 405 the learning component 310 determines if a batch of micro-feature vectors has been received, and, if not, then at step 410 the learning component 310 waits to receive another micro-feature vector before repeating step 405. If, at step 405 the learning component 310 determines that a batch of micro-feature vectors have been received, then at step 415 the learning component 310 organizes the micro-classifier feature in the SOM 315 based on the micro-feature vectors in the batch. At step 420 the learning component 310 updates the object type clusters in the SOM 315 and ART 325 based on the micro-feature vectors in the batch. Importantly, nodes in the SOM 315 corresponding to invalid micro-feature vector values are not updated with the invalid micro-feature vector values.
FIG. 4B illustrates a method for performing object type classification using the SOM-ART network component 340, according to one embodiment of the invention. At step 430 a micro-feature vector for a foreground patch is received by the micro-feature classifier 221. At step 435 the classification component 320 compares the micro-feature vector with the object type clusters that have been discovered for the scene and represented by the SOM-ART component 340. At step 440 the classification component 320 determines if the micro-feature vectors matches an object type cluster. If, in step 440 a match is found, then at step 450 the identified object type of the cluster is output for the matched micro-feature vector. If, at step 440 a match is not found, then at step 445 the classification component 320 indicates that a match was not identified for the micro-feature vector.
Advantageously, embodiments of the invention may be used as part of a computer vision engine to identify unusual events as they are observed to occur in a sequence of video frames. Importantly, what is determined as unusual need not be defined in advance, but can be determined over time by observing a stream of primitive events and a stream of context events. The SOM-ART network may generate object type clusters from the set of micro-feature vectors that are supplied to the SOM-ART network. Each cluster represents an observed statistical distribution of a particular thing or event being observed by the SOM-ART network. Importantly, the computer vision engine requires no training using predefined object definitions in order to perform the cluster discovery and object type classification. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.
Saitwal, Kishor Adinath, Xu, Gang, Seow, Ming-Jung, COBB, WESLEY KENNETH, Friedlander, David
G06K 9/6222 : with an adaptive number of ...
Clustering Nodes In A Self Organizing Map Using An Adaptive Resonance Theory Network
Sponsoring Entity: Behavioral Recognition Systems Incorporated