Kalman filter approach to augment object tracking

A computer program product stored on a non-transitory computer readable storage device for tracking one or more objects at each position in an interval in a video input with the use of a Kalman filter having a noisy measurement component and a noisy predictor component, which when executed, causes a computer to perform a method, comprising:

• obtaining a first location estimate of an object in the video input at each position in the interval, wherein the first location estimate represents the noisy measurement component of the Kalman filter;
• obtaining a second location estimate and a movement estimate of the object in the video input at each position in the interval, wherein the second location estimate and movement estimate represent the noisy predictor component of the Kalman filter;
• and
• determining a final estimate of at least one selected from the group consisting of (1) a location of the object at a future interval at each position in the interval with the Kalman filter and (2) a velocity of the object at a future interval at each position in the interval with the Kalman filter,
• wherein each final estimate is determined with reference to a Kalman gain, the Kalman gain indicating a first level of importance of the noisy measurement component and a second level of importance of the noisy predictor component, and
• wherein the first location estimate, the second location estimate, and the movement estimate each comprise estimates having two dimensional components in a Cartesian coordinate system, the two dimensional components consisting of an x dimension and a y dimension.

An object tracking system for tracking one or more objects at each position in an interval in a video input with the use of a Kalman filter having a noisy measurement component and a noisy predictor component, comprising:

• at least one memory for storing the video input;
• at least one processor in communication with the at least one memory;
• an object detecting system comprising executable code stored in the at least one memory configured to instruct the at least one processor to obtain a first location estimate of an object in the video input at each position in the interval, wherein the first location estimate represents the noisy measurement component in the Kalman filter,
• and to obtain a second location estimate and a movement estimate of the object in the video input at each position in the interval, wherein the second location estimate and movement estimate represent the noisy predictor component in the Kalman filter;
• and
• a final estimate system comprising executable code stored in to the memory configured to instruct the at least one processor to determine a final estimate of a position and/or a velocity of the object at a future time at each position in the interval with the Kalman filter, wherein each final estimate is determined with reference to a Kalman gain, the Kalman gain indicating a first level of importance of the noisy measurement component and a second level of importance of the noisy predictor component, and wherein the first location estimate, the second location estimate, and the movement estimate each comprise estimates having two dimensional components in a Cartesian coordinate system, the two dimensional components consisting of an x dimension and a y dimension.