Correlation-based tracking using tunable training and Kalman prediction

Sergio E. Ontiveros-Gallardo; Vitaly Kober

doi:10.1117/12.2237964

27 September 2016 Correlation-based tracking using tunable training and Kalman prediction

Sergio E. Ontiveros-Gallardo, Vitaly Kober

Proceedings Volume 9971, Applications of Digital Image Processing XXXIX; 997129 (2016) https://doi.org/10.1117/12.2237964
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

Tracking solves the problem of detecting and estimating the future target state in an input video sequence. In this work, an adaptive tracking algorithm by means of multiple object detections in reduced frame areas with a tunable bank of correlation filters is proposed. Prediction of the target state is carried out with the Kalman filtering. It helps us to estimate the target state, to reduce the search area in the next frame, and to solve the occlusion problem. The bank of composite filters is updated frame by frame with tolerance to different recent viewpoint and scale changes of the target. The performance of the proposed algorithm with the help of computer simulation is evaluated in terms of detection and location errors.

Citation Download Citation

Sergio E. Ontiveros-Gallardo and Vitaly Kober "Correlation-based tracking using tunable training and Kalman prediction", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997129 (27 September 2016); https://doi.org/10.1117/12.2237964

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