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Wednesday, November 15 • 11:20 - 11:40
Color-Based Object Tracking using Mean Shift and Interactive Multiple Model Kalman Filtering

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This paper presents an object tracking system that uses mean shift with Interactive Multiple Model (IMM) Kalman filtering. The system represents targets using a weighted color histogram. The target window is masked with a Gaussian kernel to approximate its probability density function (PDF). The mean shift algorithm locates the mode of a PDF by ascending its gradient. The target is iteratively tracked in each video frame using the mean shift vector. Convergence occurs when the target and target candidate have similar color histograms. Each mean shift position estimate is used as a measurement in the IMM filter. The IMM algorithm runs two Kalman filters in parallel to estimate the target location in the next frame. The estimates from each filter are mixed to yield a weighted prediction. The motion models used by the IMM filter were constant velocity and constant acceleration. The results show that pairing the IMM and mean shift methods improves tracking accuracy and computation time.


Wednesday November 15, 2017 11:20 - 11:40
Salon A/B 180 Portugal Cove Rd, St. John's, NL A1B 2N4, Canada