Multi-view cognitive swarm for object recognition and 3D tracking
First Claim
1. A multi-view object recognition system incorporating swarming domain classifiers, comprising:
- a processor having a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points, where each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions, where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold;
wherein the agents are configured to search for the object in three-dimensional (3D) spatial coordinates, such that the object is a 3D object and the 3D object has distinct appearances from each view point in the multiple view points; and
wherein the distinct appearances of the 3D object from the multiple view points are linked by agents searching for the 3D object in the spatial coordinates, such that each agent has an associated 3D location X and an object height h, and wherein each of the multiple view points is provided as a 2D image from a calibrated camera having a given geometry, such that given the known geometry of the calibrated cameras, a 2D location, [x,y]T=π
(X), of an agent'"'"'s projection in each view (2D image) is calculated and used to select an image window that is sent to a classifier having a classifier output that corresponds to the classifier'"'"'s confidence that the image window contains the object, where superscript T denotes transpose.
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Abstract
An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.
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Citations
30 Claims
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1. A multi-view object recognition system incorporating swarming domain classifiers, comprising:
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a processor having a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points, where each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions, where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold; wherein the agents are configured to search for the object in three-dimensional (3D) spatial coordinates, such that the object is a 3D object and the 3D object has distinct appearances from each view point in the multiple view points; and wherein the distinct appearances of the 3D object from the multiple view points are linked by agents searching for the 3D object in the spatial coordinates, such that each agent has an associated 3D location X and an object height h, and wherein each of the multiple view points is provided as a 2D image from a calibrated camera having a given geometry, such that given the known geometry of the calibrated cameras, a 2D location, [x,y]T=π
(X), of an agent'"'"'s projection in each view (2D image) is calculated and used to select an image window that is sent to a classifier having a classifier output that corresponds to the classifier'"'"'s confidence that the image window contains the object, where superscript T denotes transpose. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer implemented method for multi-view object recognition using swarming domain classifiers, the method comprising acts of:
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configuring a plurality of software agents (i.e., particles) to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points, where each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions; configuring each agent to perform at least one iteration, the iteration being a search in the solution space for a potential solution optimum where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold; further comprising an act of configuring the agents to search for the object in three-dimensional (3D) spatial coordinates, such that the object is a 3D object and the 3D object has distinct appearances from each view point in the multiple view points; and further comprising an act of linking the agents such that the distinct appearances of the 3D object from the multiple view points are linked by agents searching for the 3D object in the spatial coordinates, such that each agent has an associated 3D location X and an object height h, and wherein each of the multiple view points is provided as a 2D image from a calibrated camera having a given geometry, such that given the known geometry of the calibrated cameras, a 2D location, [x,y]T=π
(X), of an agent'"'"'s projection in each view (2D image) is calculated and used to select an image window that is sent to a classifier having a classifier output that corresponds to the classifier'"'"'s confidence that the image window contains the object, where superscript T denotes transpose. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer program product for object recognition, the computer program product comprising computer-readable instruction means encoded on a computer-readable medium and executable by a computer for causing a computer to:
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configure a plurality of software agents (i.e., particles) to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points, where each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions, where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold; further comprising instruction means to cause a computer to perform an operation of utilizing the agents to search for the object in three-dimensional (3D) spatial coordinates, such that the object is a 3D object and the 3D object has distinct appearances from each view point in the multiple view points; and further comprising instruction means to cause a computer to perform an operation of linking the agents such that the distinct appearances of the 3D object from the multiple view points are linked by agents searching for the 3D object in the spatial coordinates, such that each agent has an associated 3D location X and an object height h, and wherein each of the multiple view points is provided as a 2D image from a calibrated camera having a given geometry, such that given the known geometry of the calibrated cameras, a 2D location, [x,y]T=π
(X), of an agent'"'"'s projection in each view (2D image) is calculated and used to select an image window that is sent to a classifier having a classifier output that corresponds to the classifier'"'"'s confidence that the image window contains the object, where superscript T denotes transpose. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification