Multi-view cognitive swarm for object recognition and 3D tracking

A method for multi-view object recognition using swarming domain classifiers, the method comprising acts of:

• 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.

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:

• 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.