A novel RANSAC-based Kalman filter algorithm for object characterization and tracking

Sumit Chakravarty; Chaoqun Dong; Boyu Wang; Madhushri Banerjee

doi:10.1117/12.2048692

28 May 2014 A novel RANSAC-based Kalman filter algorithm for object characterization and tracking

Sumit Chakravarty, Chaoqun Dong, Boyu Wang, Madhushri Banerjee

Proceedings Volume 9120, Mobile Multimedia/Image Processing, Security, and Applications 2014; 91200D (2014) https://doi.org/10.1117/12.2048692
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Advanced Kalman Filters has been used extensively in the domain of video based tracking of target objects. They can be viewed as an extension of Kalman Filtering principle. Instead of using object point mass as a tracker as used in the Kalman filter, alterations are made to incorporate advanced strategies. This is the typical formulation of the Kalman Enhanced Filter (KEF). Even though this allows the use of non-linearity for state prediction, it is constrained by its choice of the Kalman state transition function. Furthermore the KEF does not provide a methodology of selection of the distribution of the prior. The proper tuning of the above choices is critical for performance of the KEF. This work addresses these constraints of the KEF. It particularly targets two significant areas. Firstly it automates the state matrix generation process by fusing alternate tracking mechanism to the KEF. This novel technique is tested for tracking of real video sequence and its efficacy is quantified.

Citation Download Citation

Sumit Chakravarty, Chaoqun Dong, Boyu Wang, and Madhushri Banerjee "A novel RANSAC-based Kalman filter algorithm for object characterization and tracking", Proc. SPIE 9120, Mobile Multimedia/Image Processing, Security, and Applications 2014, 91200D (28 May 2014); https://doi.org/10.1117/12.2048692

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