Title :
Improving the performance of Kalman filter for hand tracking in Persian sign language video
Author :
Zadghorban, Masoud ; Nahvi, Manoochehr
Abstract :
Hand tracking is one of the most important phases of a sign language recognition system that affects the final recognition rate directly. Kalman filter is a well-known technique for object tracking. By minimizing the mean square error, this filter is able to estimate the past, present and future states in a process, even in systems that are inherently uncertain. Hand movement in sign language video is very complex. Hence, Kalman filter is a suitable estimator to predict the hands motion. In this paper, we present an approach to optimize the Kalman filter to track the movement of hands accurately. The modified Kalman filter is then compared with other tracking methods by testing on the Persian sign language video database made by authors.
Keywords :
Kalman filters; mean square error methods; motion estimation; object tracking; sign language recognition; video signal processing; Kalman filter; Persian sign language video; hand movement; hand tracking; hands motion prediction estimator; mean square error minimization; movement tracking; object tracking; recognition rate; sign language recognition system; Color; Filtering algorithms; Filtering theory; Kalman filters; Mathematical model; Skin; Tracking; Kalman filter; Persian sign language video; motion features; skin color feature;
Conference_Titel :
Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on
Conference_Location :
Rasht
Print_ISBN :
978-1-4799-8444-2
DOI :
10.1109/PRIA.2015.7161629